Welcome to the GSFC Exoplanet Modeling and Analysis Center (EMAC)

EMAC serves as a catalog, repository and integration platform for modeling and analysis resources focused on the study of exoplanet characteristics and environments. EMAC is a key project of the GSFC Sellers Exoplanet Environments Collaboration (SEEC).

More Information on EMAC for first-time visitors...

If you make use of tools linked or hosted on EMAC: please use the following statement in your publication acknowledgements: “This research made use of the NASA Exoplanet Modeling and Analysis Center (EMAC), which is funded by the NASA Planetary Science Division’s Internal Scientist Funding Model.”

Stay up to date with EMAC!
  • Subscribe to our monthly RSS messages on new updates and tools
  • Check out the Twitter account @ExoplanetModels (not an official NASA account), where new tools and features are highlighted
Help us improve EMAC!
  • Email us with general feedback at and tell us what you’d change or improve.
  • Click the icon in a resource box to provide suggestions for an individual tool or tools.
Other EMAC info!
  • EMAC is intended as a clearinghouse for the whole research community interested in exoplanets, where any software or model developer can submit their tool/model or their model output as a contribution for others to use.
  • EMAC provides a searchable and sortable database for available source code and data output files - both resources hosted locally by EMAC as well as existing external tools and repositories hosted elsewhere.
  • The EMAC team also helps develop new web interfaces for tools that can be run “on-demand” or model grids that can be interpolated for more individualized results.
  • If you would like to submit a new tool/model to EMAC, please visit our Submit a Resource page.
  • For help with tutorials for select resources/tools use the “Demo” buttons below and subscribe to our YouTube channel.
  • Watch this video for a walk-through of the whole EMAC site, including how to submit a new tool and how to access information for each resource.

The P.I. is Avi Mandell, and the Deputy P.I. is Eric Lopez; more information on EMAC staffing and organization can be found on Our Team page.

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MARGE: A Python package to train and evaluate neural networks

Himes et al.

MARGE (Machine learning Algorithm for Radiative transfer of Generated Exoplanets) is an all-in-one package to generate exoplanet spectra across a defined parameter space, process the output, and train machine learning (ML) models as a fast approximation to radiative transfer (RT). Despite its backronym name, MARGE is a general package that can train neural networks on a provided data set of inputs and outputs. MARGE is an open-source project under the Reproducible Research Software License and welcomes improvements from the community to be submitted via pull requests on Github.

Code Language(s): Python3

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MARGE: A Python package to train and evaluate neural networks

Himes et al.

MARGE (Machine learning Algorithm for Radiative transfer of Generated Exoplanets) is an all-in-one package to generate exoplanet spectra across a defined parameter space, process the output, and train machine learning (ML) models as a fast approximation to radiative transfer (RT). Despite its backronym name, MARGE is a general package that can train neural networks on a provided data set of inputs and outputs. MARGE is an open-source project under the Reproducible Research Software License and welcomes improvements from the community to be submitted via pull requests on Github.

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HOMER: A Bayesian inverse modeling code

Himes et al.

HOMER (the Helper Of My Eternal Retrievals) is a Bayesian inverse modeling code. Given some data and uncertainties, the posterior distribution is determined for some model. HOMER allows for both nested sampling and Markov chain Monte Carlo (MCMC) frameworks. HOMER's forward model is a neural network (NN) surrogate model trained by MARGE. For details on MARGE, see the MARGE User Manual at https://exosports.github.io/MARGE/doc/MARGE_User_Manual.html. HOMER is released under the Reproducible Research Software License and welcomes community contributions via pull requests on Github.

Code Language(s): Python3

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HOMER: A Bayesian inverse modeling code

Himes et al.

HOMER (the Helper Of My Eternal Retrievals) is a Bayesian inverse modeling code. Given some data and uncertainties, the posterior distribution is determined for some model. HOMER allows for both nested sampling and Markov chain Monte Carlo (MCMC) frameworks. HOMER's forward model is a neural network (NN) surrogate model trained by MARGE. For details on MARGE, see the MARGE User Manual at https://exosports.github.io/MARGE/doc/MARGE_User_Manual.html. HOMER is released under the Reproducible Research Software License and welcomes community contributions via pull requests on Github.

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Aladin: Aladin Sky Atlas

CDS - Centre de Données astronomiques de Strasourg, Université de Strasbourg/CNRS

Aladin is an interactive sky atlas allowing the user to visualize digitized astronomical images or full surveys, superimpose entries from astronomical catalogues or databases, and interactively access related data and information from the Simbad database, the VizieR service and other archives for all known astronomical objects in the field. The Aladin sky atlas is available in two modes: Aladin Desktop, a regular application and Aladin Lite an HTML5 javascript web widget.

Code Language(s): java, javascript

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Aladin: Aladin Sky Atlas

CDS - Centre de Données astronomiques de Strasourg, Université de Strasbourg/CNRS

Aladin is an interactive sky atlas allowing the user to visualize digitized astronomical images or full surveys, superimpose entries from astronomical catalogues or databases, and interactively access related data and information from the Simbad database, the VizieR service and other archives for all known astronomical objects in the field. The Aladin sky atlas is available in two modes: Aladin Desktop, a regular application and Aladin Lite an HTML5 javascript web widget.

About Demo
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FitsMap: A Simple, Lightweight Tool For Displaying Interactive Astronomical Image and Catalog Data

Ryan Hausen and Brant E. Robertson

The visual inspection of image and catalog data continues to be a valuable aspect of astronomical data analysis. As the scale of astronomical image and catalog data continues to grow, visualizing the data becomes increasingly difficult. In this work, we introduce FitsMap, a simple, lightweight tool for visualizing astronomical image and catalog data. FitsMap only requires a simple web server and can scale to over gigapixel images with tens of millions of sources. Further, the web-based visualizations can be viewed performantly on mobile devices. FitsMap is implemented in Python and is open source (https://github.com/ryanhausen/fitsmap).

Code Language(s): python3,javascript

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FitsMap: A Simple, Lightweight Tool For Displaying Interactive Astronomical Image and Catalog Data

Ryan Hausen and Brant E. Robertson

The visual inspection of image and catalog data continues to be a valuable aspect of astronomical data analysis. As the scale of astronomical image and catalog data continues to grow, visualizing the data becomes increasingly difficult. In this work, we introduce FitsMap, a simple, lightweight tool for visualizing astronomical image and catalog data. FitsMap only requires a simple web server and can scale to over gigapixel images with tens of millions of sources. Further, the web-based visualizations can be viewed performantly on mobile devices. FitsMap is implemented in Python and is open source (https://github.com/ryanhausen/fitsmap).

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exocartographer: Forward-model for constraining exoplanet maps and orbital parameters from reflected lightcurves

Farr, B., Farr, W., Cowan, N., Haggard, H., and Robinson, T.

A flexible, open-source, Bayesian framework for solving the exo-cartography inverse problem. The map is parameterized with equal-area HEALPix pixels with a Gaussian Process regularization.

Code Language(s): Python3

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exocartographer: Forward-model for constraining exoplanet maps and orbital parameters from reflected lightcurves

Farr, B., Farr, W., Cowan, N., Haggard, H., and Robinson, T.

A flexible, open-source, Bayesian framework for solving the exo-cartography inverse problem. The map is parameterized with equal-area HEALPix pixels with a Gaussian Process regularization.

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SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

Tabernero, H. M. et al.

SteParSyn is a Python code designed to infer the stellar atmospheric parameters Teff, log(g), and [Fe/H] of late-type stars (FGKM) using the spectral synthesis method. It uses a Markov chain Monte Carlo (MCMC) sampler to explore the parameter space by comparing synthetic spectra to the observations. The code has been employed to study stars in open clusters, cepheids, stars in the Magellanic clouds, exoplanet hosts observed with ESPRESSO and CARMENES, and to characterize the first super-AGB candidate in our Galaxy. The code is available to the community in a GitHub repository.

Code Language(s): Python3

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SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

Tabernero, H. M. et al.

SteParSyn is a Python code designed to infer the stellar atmospheric parameters Teff, log(g), and [Fe/H] of late-type stars (FGKM) using the spectral synthesis method. It uses a Markov chain Monte Carlo (MCMC) sampler to explore the parameter space by comparing synthetic spectra to the observations. The code has been employed to study stars in open clusters, cepheids, stars in the Magellanic clouds, exoplanet hosts observed with ESPRESSO and CARMENES, and to characterize the first super-AGB candidate in our Galaxy. The code is available to the community in a GitHub repository.

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PyExoRaMa: An Interactive Tool in Python to Investigate the Radius-Mass Diagram for Exoplanets

Amadori, F.; Damasso, M.; Zeng, L.; Sozzetti, A.

This is the python version of the software originally developed with Mathematica by Zeng et al (https://doi.org/10.5281/zenodo.5899463). The code represents a useful tool for visualizing and manipulating data related to extrasolar planets and their host stars in a multi-dimensional parameter space. It can be used to identify possible interdependence among several physical parameters and to compare observables with theoretical models describing the exoplanet composition and structure. Our transposition to Python presents some new features with respect to the original version

Code Language(s): Python3

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PyExoRaMa: An Interactive Tool in Python to Investigate the Radius-Mass Diagram for Exoplanets

Amadori, F.; Damasso, M.; Zeng, L.; Sozzetti, A.

This is the python version of the software originally developed with Mathematica by Zeng et al (https://doi.org/10.5281/zenodo.5899463). The code represents a useful tool for visualizing and manipulating data related to extrasolar planets and their host stars in a multi-dimensional parameter space. It can be used to identify possible interdependence among several physical parameters and to compare observables with theoretical models describing the exoplanet composition and structure. Our transposition to Python presents some new features with respect to the original version

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special: SPEctral Characterization of ImAged Low-mass companions

Valentin Christiaens

special is a package developed for the spectral characterization of directly imaged low-mass companions (MLT dwarfs). Nonetheless, this toolkit can also be used in a more general way for the characterisation of any object with a measured spectrum, provided an input model or template grid. The available tools range from the calculation of spectral covariance matrices (e.g. for IFS datacubes) and empirical spectral indices to the Bayesian inference of atmospheric parameters provided an input grid of models. In the latter case, an MCMC or nested sampler can be used, and additional parameters such as (extra) black body component(s) and extinction can be considered.

Code Language(s): Python3

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special: SPEctral Characterization of ImAged Low-mass companions

Valentin Christiaens

special is a package developed for the spectral characterization of directly imaged low-mass companions (MLT dwarfs). Nonetheless, this toolkit can also be used in a more general way for the characterisation of any object with a measured spectrum, provided an input model or template grid. The available tools range from the calculation of spectral covariance matrices (e.g. for IFS datacubes) and empirical spectral indices to the Bayesian inference of atmospheric parameters provided an input grid of models. In the latter case, an MCMC or nested sampler can be used, and additional parameters such as (extra) black body component(s) and extinction can be considered.

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GENGA: A GPU N-body integrator for planet formation and planetary system evolution

Simon Grimm, Joachim Stadel

GENGA is a hybrid symplectic N-body integrator, designed to integrate planet and planetesimal dynamics in the late stage of planet formation and stability analysis of planetary systems. GENGA is based on the integration scheme of the Mercury code (Chambers 1999), which handles close encounters with very good energy conservation. The GENGA code supports three simulation modes: Integration of up to 60000 - 100000 massive bodies, integration with up to a million test particles, or parallel integration of a large number of individual planetary systems. GENGA is written in CUDA C and runs on all NVidia GPUs with compute capability of at least 2.0.

Code Language(s): CUDA, C

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GENGA: A GPU N-body integrator for planet formation and planetary system evolution

Simon Grimm, Joachim Stadel

GENGA is a hybrid symplectic N-body integrator, designed to integrate planet and planetesimal dynamics in the late stage of planet formation and stability analysis of planetary systems. GENGA is based on the integration scheme of the Mercury code (Chambers 1999), which handles close encounters with very good energy conservation. The GENGA code supports three simulation modes: Integration of up to 60000 - 100000 massive bodies, integration with up to a million test particles, or parallel integration of a large number of individual planetary systems. GENGA is written in CUDA C and runs on all NVidia GPUs with compute capability of at least 2.0.

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SOAP 2.0: RV stellar activity simulation including spot and faculae

X. Dumusque, I. Boisse, N.Santos

SOAP 2.0 allows to model the RV effect (and the effect on the FWHM, CONTRAST and BIS SPAN of the CCF) induced by magnetic regions (spot and faculae) on the surface of a star. The temperature of the magnetic features, their size, their location on the stellar surface, the resolution of the instrument used, the stellar properties (radius, effective temperature, inclination), are all parameters that can be adjusted. The code is efficient, as all the backend computation are performed in C, and is friendly to use as the interface is in python.

Code Language(s): C, Python2

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SOAP 2.0: RV stellar activity simulation including spot and faculae

X. Dumusque, I. Boisse, N.Santos

SOAP 2.0 allows to model the RV effect (and the effect on the FWHM, CONTRAST and BIS SPAN of the CCF) induced by magnetic regions (spot and faculae) on the surface of a star. The temperature of the magnetic features, their size, their location on the stellar surface, the resolution of the instrument used, the stellar properties (radius, effective temperature, inclination), are all parameters that can be adjusted. The code is efficient, as all the backend computation are performed in C, and is friendly to use as the interface is in python.

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NbodyGradient: Differentiable symplectic N-body code for arbitrary orbital architectures

Agol, E., Hernandez, D. & Langford, Z.

NbodyGradient is a symplectic integrator for Newtonian gravity and arbitrary N-body hierarchies. It computes the derivatives of the output with respect to the input coordinates with high numerical precision. It was developed for transit-timing analyses, and is the first code to give derivatives of the transit times with respect to the initial conditions (either masses & cartesian coordinates/velocities or orbital elements). It is written in the Julia language. It is being extended for modeling RV, astrometry, and photodynamics.

Code Language(s): Julia

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NbodyGradient: Differentiable symplectic N-body code for arbitrary orbital architectures

Agol, E., Hernandez, D. & Langford, Z.

NbodyGradient is a symplectic integrator for Newtonian gravity and arbitrary N-body hierarchies. It computes the derivatives of the output with respect to the input coordinates with high numerical precision. It was developed for transit-timing analyses, and is the first code to give derivatives of the transit times with respect to the initial conditions (either masses & cartesian coordinates/velocities or orbital elements). It is written in the Julia language. It is being extended for modeling RV, astrometry, and photodynamics.

About Demo
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Roman Coronagraph Exposure Time Calculator: Estimates integration times for the Roman Coronagraph instrument

© 2021. Government sponsorship acknowledged. The research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

The Roman Coronagraph Exposure Time Calculator (Roman_Coronagraph_ETC for short) is the public version of the exposure time calculator of the Coronagraph Instrument aboard the Nancy Grace Roman Space Telescope funded by NASA. Roman_Coronagraph_ETC methods are based upon peer reviewed research articles and a collection of instrumental and modeling parameters of both the Coronagraph Instrument and the Nancy Grace Roman Space Telescope. The values in these files do not contain any ITAR or export control information. Roman_Coronagraph_ETC is licensed under Apache v2.

Code Language(s): Python3

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Roman Coronagraph Exposure Time Calculator: Estimates integration times for the Roman Coronagraph instrument

© 2021. Government sponsorship acknowledged. The research was carried out in part at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

The Roman Coronagraph Exposure Time Calculator (Roman_Coronagraph_ETC for short) is the public version of the exposure time calculator of the Coronagraph Instrument aboard the Nancy Grace Roman Space Telescope funded by NASA. Roman_Coronagraph_ETC methods are based upon peer reviewed research articles and a collection of instrumental and modeling parameters of both the Coronagraph Instrument and the Nancy Grace Roman Space Telescope. The values in these files do not contain any ITAR or export control information. Roman_Coronagraph_ETC is licensed under Apache v2.

About Demo
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RASSINE: A tool for stellar spectrum continuum fitting

Michael Cretignier

RASSINE is a Python3 code dedicated to fitting stellar continuum using an alpha-shape algorithm. The code is divided in a sequence of five consecutive steps (SNAKE) : 1) Smoothing 2) Neighbourhood local maxima 3) Alpha-shape 4) Killing outliers 5) Envelop interpolation The code contains a fully automatic mode in case of 1d spectrum is given as input but the stellar spectrum can be also fitted by hand using the GUI interface.

Code Language(s): Python3

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RASSINE: A tool for stellar spectrum continuum fitting

Michael Cretignier

RASSINE is a Python3 code dedicated to fitting stellar continuum using an alpha-shape algorithm. The code is divided in a sequence of five consecutive steps (SNAKE) : 1) Smoothing 2) Neighbourhood local maxima 3) Alpha-shape 4) Killing outliers 5) Envelop interpolation The code contains a fully automatic mode in case of 1d spectrum is given as input but the stellar spectrum can be also fitted by hand using the GUI interface.

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SISTER: Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance

S.R. Hildebrandt and S.B. Shaklan are the principal authors of SISTER

The Starshade Imaging Simulations tool is a versatile tool designed to provide enough accuracy and variety when predicting how an exoplanet system would look like in an instrument that utilizes an Starshade to block the light from the host star. The tool allows for controlling a set of parameters of the whole instrument that have to do with: (1) the Starshade design, (2) the exoplanetary system, (3) the optical system (telescope) and (4) the detector (camera). There is a built-in plotting software added, but the simulations may be stored on disk and be plotted with any other software. SISTER has an online tutorial and has been published in JATIS. Visit sister.caltech.edu for details.

Code Language(s): Matlab

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SISTER: Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance

S.R. Hildebrandt and S.B. Shaklan are the principal authors of SISTER

The Starshade Imaging Simulations tool is a versatile tool designed to provide enough accuracy and variety when predicting how an exoplanet system would look like in an instrument that utilizes an Starshade to block the light from the host star. The tool allows for controlling a set of parameters of the whole instrument that have to do with: (1) the Starshade design, (2) the exoplanetary system, (3) the optical system (telescope) and (4) the detector (camera). There is a built-in plotting software added, but the simulations may be stored on disk and be plotted with any other software. SISTER has an online tutorial and has been published in JATIS. Visit sister.caltech.edu for details.

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spectools_ir: A suite of tools designed for analysis of medium/high-resolution IR molecular spectra

Colette Salyk

Spectools_ir is a small suite of tools designed for analysis of medium/high-resolution IR molecular astronomical spectra. It consists of three main sub-modules (flux_calculator, slabspec, and slab_fitter) as well as a 'utils' sub-module, with a few additional functions. Spectools_ir was written with infrared medium/high-resolution molecular spectroscopy in mind. It often assumes spectra are in units of Jy and microns, and it uses information from the HITRAN molecular database. Some routines are more general, but users interested in other applications should proceed with caution.

Code Language(s): Python3

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spectools_ir: A suite of tools designed for analysis of medium/high-resolution IR molecular spectra

Colette Salyk

Spectools_ir is a small suite of tools designed for analysis of medium/high-resolution IR molecular astronomical spectra. It consists of three main sub-modules (flux_calculator, slabspec, and slab_fitter) as well as a 'utils' sub-module, with a few additional functions. Spectools_ir was written with infrared medium/high-resolution molecular spectroscopy in mind. It often assumes spectra are in units of Jy and microns, and it uses information from the HITRAN molecular database. Some routines are more general, but users interested in other applications should proceed with caution.

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ROCKE-3D: A fully coupled ocean atmosphere 3-D General Circulation Model

Way et al. 2017

Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D) is a three-dimensional General Circulation Model (GCM) developed at the NASA Goddard Institute for Space Studies for the modeling of atmospheres and oceans of solar system and exoplanetary terrestrial planets.

Code Language(s): Fortran

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ROCKE-3D: A fully coupled ocean atmosphere 3-D General Circulation Model

Way et al. 2017

Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D) is a three-dimensional General Circulation Model (GCM) developed at the NASA Goddard Institute for Space Studies for the modeling of atmospheres and oceans of solar system and exoplanetary terrestrial planets.

About Demo
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BASTA: The BAyesian STellar Algorithm

The BASTA team

BASTA is a python-based fitting tool designed to determine properties of stars using a pre-computed grid of stellar models. It calculates the probability density function of a given stellar property based on a set of observational constraints defined by the user.

Code Language(s): Python3, Fortran

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BASTA: The BAyesian STellar Algorithm

The BASTA team

BASTA is a python-based fitting tool designed to determine properties of stars using a pre-computed grid of stellar models. It calculates the probability density function of a given stellar property based on a set of observational constraints defined by the user.

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dips: Detrending strictly periodic signals

A. Prsa

dips is an algorithm for detrending timeseries of strictly periodic signals. It does not assume any functional form for the signal or the background or the noise; it disentangles the strictly periodic component from everything else. We use it in astronomy for detrending Kepler, K2 and TESS timeseries of periodic variable stars, eclipsing binary stars, exoplanets etc. The algorithm is described in detail in Prša, Zhang and Wells (2019), PASP 131, 8001. A new, generalized version of dips will be explained in Horvat and Prša (2022), currently in preparation.

Code Language(s): Python3

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dips: Detrending strictly periodic signals

A. Prsa

dips is an algorithm for detrending timeseries of strictly periodic signals. It does not assume any functional form for the signal or the background or the noise; it disentangles the strictly periodic component from everything else. We use it in astronomy for detrending Kepler, K2 and TESS timeseries of periodic variable stars, eclipsing binary stars, exoplanets etc. The algorithm is described in detail in Prša, Zhang and Wells (2019), PASP 131, 8001. A new, generalized version of dips will be explained in Horvat and Prša (2022), currently in preparation.

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RADIS: Fast Line-by-line code for infrared emission & absorption spectra at equilibrium & non-LTE

Pannier, E. ; Bekerom D. v. d ; Minesi N. and the RADIS contributors

RADIS uses a new algorithm that can resolve spectra with millions of lines within seconds on a single-CPU, and can be GPU-accelerated for almost-instant-computation (up to 5e14 lines*spectral points/s). It supports HITRAN, HITEMP and ExoMol out-of-the-box (auto-download), and therefore is particularly suitable to compute cross-sections or transmission spectra at high-temperature. It includes equilibrium calculations for all species, and non-LTE for CO2 and CO. The code is an open-source Python library (https://github.com/radis/radis) and can also be executed in an online environment with pre-configured HITEMP databases (https://radis.github.io/radis-lab/).

Code Language(s): Python3

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RADIS: Fast Line-by-line code for infrared emission & absorption spectra at equilibrium & non-LTE

Pannier, E. ; Bekerom D. v. d ; Minesi N. and the RADIS contributors

RADIS uses a new algorithm that can resolve spectra with millions of lines within seconds on a single-CPU, and can be GPU-accelerated for almost-instant-computation (up to 5e14 lines*spectral points/s). It supports HITRAN, HITEMP and ExoMol out-of-the-box (auto-download), and therefore is particularly suitable to compute cross-sections or transmission spectra at high-temperature. It includes equilibrium calculations for all species, and non-LTE for CO2 and CO. The code is an open-source Python library (https://github.com/radis/radis) and can also be executed in an online environment with pre-configured HITEMP databases (https://radis.github.io/radis-lab/).

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Isca: Idealized global circulation modeling: A flexible GCM for modelling planetary atmospheres.

The Isca Team

Isca is a framework for the construction of models of the global circulation of planetary atmospheres at varying levels of realism and complexity. Isca itself is not a single model, nor is it intended to provide a fully ‘comprehensive’ model capable of weather forecasts or climate projections for policy use. Rather, our intent is to enable the user to make appropriate models for the planet or problem of interest. Isca can and has been used for Earth, Mars, Jupiter, Titan and various exoplanets.

Code Language(s): Fortran, Python3

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Isca: Idealized global circulation modeling: A flexible GCM for modelling planetary atmospheres.

The Isca Team

Isca is a framework for the construction of models of the global circulation of planetary atmospheres at varying levels of realism and complexity. Isca itself is not a single model, nor is it intended to provide a fully ‘comprehensive’ model capable of weather forecasts or climate projections for policy use. Rather, our intent is to enable the user to make appropriate models for the planet or problem of interest. Isca can and has been used for Earth, Mars, Jupiter, Titan and various exoplanets.

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LDTk: Limb Darkening Toolkit

Hannu Parviainen

LDTk is a Python toolkit for calculating stellar limb darkening profiles and model-specific limb darkening coefficients using the stellar atmosphere spectrum library by Husser et al. (2013). The first version of the toolkit was described in Parviainen & Aigrain, MNRAS 453, 3821–3826 (2015), and the latest version (v1.4, published in August 2020) contains several speed and usability improvements.

Code Language(s): Python3

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LDTk: Limb Darkening Toolkit

Hannu Parviainen

LDTk is a Python toolkit for calculating stellar limb darkening profiles and model-specific limb darkening coefficients using the stellar atmosphere spectrum library by Husser et al. (2013). The first version of the toolkit was described in Parviainen & Aigrain, MNRAS 453, 3821–3826 (2015), and the latest version (v1.4, published in August 2020) contains several speed and usability improvements.

Demo
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PyTransit: Fast and easy exoplanet transit light curve modelling in Python

Hannu Parviainen

PyTransit offers optimised CPU and GPU implementations of popular exoplanet transit models with a unified interface, and thrives to be the fastest and the most versatile tool for transit modelling in Python. PyTransit makes transit model evaluation trivial whether modelling straightforward single-passband transit light curves or more complex science-cases, such as transmission spectroscopy or heterogeneous data sets. Further, the models are can be evaluated for a large number of parameter sets in parallel to optimize the evaluation speed with population-based MCMC samplers such as emcee. PyTransit has been used in research since 2010, and continues to be under active development in 2022.

Code Language(s): Python3

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PyTransit: Fast and easy exoplanet transit light curve modelling in Python

Hannu Parviainen

PyTransit offers optimised CPU and GPU implementations of popular exoplanet transit models with a unified interface, and thrives to be the fastest and the most versatile tool for transit modelling in Python. PyTransit makes transit model evaluation trivial whether modelling straightforward single-passband transit light curves or more complex science-cases, such as transmission spectroscopy or heterogeneous data sets. Further, the models are can be evaluated for a large number of parameter sets in parallel to optimize the evaluation speed with population-based MCMC samplers such as emcee. PyTransit has been used in research since 2010, and continues to be under active development in 2022.

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SERVAL: Spectrum radial velocity analyser

Mathias Zechmeister

Serval measures and analyses precise radial velocities in stellar spectra using least square fitting.

Code Language(s): Python2, Python3, C, Fortran

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SERVAL: Spectrum radial velocity analyser

Mathias Zechmeister

Serval measures and analyses precise radial velocities in stellar spectra using least square fitting.

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MAGRATHEA: Planetary interior structure code

Huang, C., Rice D.R., and Steffen, J. H.

A planet structure code which considers the case of fully differentiated interiors. The code integrates the hydrostatic equation in order to shoot for the correct planet radius given the mass in each layer. The code returns the pressure, temperature, density, phase, and radius at steps of enclosed mass. The code support 4 layers: core, mantle, hydrosphere, and atmosphere. Each layer has a phase diagram with equations of state chosen for each phase. Users can easily adjust the model to their preferred phase diagram and equations of state.

Code Language(s): C++, Python3

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MAGRATHEA: Planetary interior structure code

Huang, C., Rice D.R., and Steffen, J. H.

A planet structure code which considers the case of fully differentiated interiors. The code integrates the hydrostatic equation in order to shoot for the correct planet radius given the mass in each layer. The code returns the pressure, temperature, density, phase, and radius at steps of enclosed mass. The code support 4 layers: core, mantle, hydrosphere, and atmosphere. Each layer has a phase diagram with equations of state chosen for each phase. Users can easily adjust the model to their preferred phase diagram and equations of state.

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P-winds: An open-source Python code to model planetary outflows and upper atmospheres

Leonardo A. Dos Santos, Aline A. Vidotto, Shreyas Vissapragada, et al.

Python implementation of Parker wind models for planetary atmospheres. The main goal of this code is to produce simplified, 1-D models of the upper atmosphere of a planet, and perform radiative transfer to calculate observable spectral signatures. The scalable implementation of 1D models allows for atmospheric retrievals to calculate atmospheric escape rates and temperatures. In addition, the modular implementation allows for a smooth plugging-in of more complex descriptions to forward model their corresponding spectral signatures (e.g., self-consistent or 3D models).

Code Language(s): Python3

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P-winds: An open-source Python code to model planetary outflows and upper atmospheres

Leonardo A. Dos Santos, Aline A. Vidotto, Shreyas Vissapragada, et al.

Python implementation of Parker wind models for planetary atmospheres. The main goal of this code is to produce simplified, 1-D models of the upper atmosphere of a planet, and perform radiative transfer to calculate observable spectral signatures. The scalable implementation of 1D models allows for atmospheric retrievals to calculate atmospheric escape rates and temperatures. In addition, the modular implementation allows for a smooth plugging-in of more complex descriptions to forward model their corresponding spectral signatures (e.g., self-consistent or 3D models).

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TULIPS: Tool for Understanding the Lives, Interiors, and Physics of Stars

Laplace, E.

TULIPS creates diagrams of the structure and evolution of stars based on the output of one-dimensional stellar evolution simulations and is optimized for MESA. Instead of complex diagrams, TULIPS represents stars as circles of varying size and color. TULIPS' capabilities include visualizing the size and perceived color of stars, their interior mixing and nuclear burning processes, their chemical composition, and comparing different stellar structures. TULIPS is described in this paper. Examples and tutorials can be found here.

Code Language(s): Python3

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TULIPS: Tool for Understanding the Lives, Interiors, and Physics of Stars

Laplace, E.

TULIPS creates diagrams of the structure and evolution of stars based on the output of one-dimensional stellar evolution simulations and is optimized for MESA. Instead of complex diagrams, TULIPS represents stars as circles of varying size and color. TULIPS' capabilities include visualizing the size and perceived color of stars, their interior mixing and nuclear burning processes, their chemical composition, and comparing different stellar structures. TULIPS is described in this paper. Examples and tutorials can be found here.

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PyMieDAP: Radiative Transfer of Polarized Light in Planetary Atmospheres

Rossi, L. et al.

PyMieDAP (Python Mie Doubling Adding Program) is a Fortran-Python package to make light scattering computations with Mie scattering and radiative transfer computations with full orders of scattering, using the Doubling-Adding method. PyMieDAP takes into account the polarization of the light scattered. Full planet modeling at any phase angle is possible. Inhomogeneous planets can be modeled. With the subpackage exopy, it is also possible to simulate systems with a star, a planet and a possible moon.

Code Language(s): Python3

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PyMieDAP: Radiative Transfer of Polarized Light in Planetary Atmospheres

Rossi, L. et al.

PyMieDAP (Python Mie Doubling Adding Program) is a Fortran-Python package to make light scattering computations with Mie scattering and radiative transfer computations with full orders of scattering, using the Doubling-Adding method. PyMieDAP takes into account the polarization of the light scattered. Full planet modeling at any phase angle is possible. Inhomogeneous planets can be modeled. With the subpackage exopy, it is also possible to simulate systems with a star, a planet and a possible moon.

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TidalPy: Software Toolbox for Estimating Tidal Heating and Dynamics in Solar System Moons and Exoplanets

Joe Renaud

TidalPy is an open-source software suite designed to assist researchers in the semi-analytic calculation of tidal dissipation and subsequent orbit-spin evolution for rocky and icy worlds. TidalPy serves as simple to install (cross-platform) and, hopefully, simple to use package that users can pick up and hit the ground running to answer basic questions about tidal dynamics.

Code Language(s): Python3

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TidalPy: Software Toolbox for Estimating Tidal Heating and Dynamics in Solar System Moons and Exoplanets

Joe Renaud

TidalPy is an open-source software suite designed to assist researchers in the semi-analytic calculation of tidal dissipation and subsequent orbit-spin evolution for rocky and icy worlds. TidalPy serves as simple to install (cross-platform) and, hopefully, simple to use package that users can pick up and hit the ground running to answer basic questions about tidal dynamics.

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Nii: A Bayesian orbit retrieval code applied to differential astrometry

Sheng Jin

Here we present an open-source Python-based Bayesian orbit retrieval code (Nii) that implements an automatic parallel tempering Markov chain Monte Carlo (APT-MCMC) strategy. Nii provides a module to simulate the observations of a space-based astrometry mission in the search for exoplanets, a signal extraction process for differential astrometric measurements using multiple reference stars, and an orbital parameter retrieval framework using APT-MCMC. We further verify the orbit retrieval ability of the code through two examples corresponding to a single-planet system and a dual-planet system. In both cases, efficient convergence on the posterior probability distribution can be achieved.

Code Language(s): Python3

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Nii: A Bayesian orbit retrieval code applied to differential astrometry

Sheng Jin

Here we present an open-source Python-based Bayesian orbit retrieval code (Nii) that implements an automatic parallel tempering Markov chain Monte Carlo (APT-MCMC) strategy. Nii provides a module to simulate the observations of a space-based astrometry mission in the search for exoplanets, a signal extraction process for differential astrometric measurements using multiple reference stars, and an orbital parameter retrieval framework using APT-MCMC. We further verify the orbit retrieval ability of the code through two examples corresponding to a single-planet system and a dual-planet system. In both cases, efficient convergence on the posterior probability distribution can be achieved.

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EVolve: Growth and evolution of volcanically-derived atmospheres

Philippa Liggins, incorporating the FastChem 2.0 model of Daniel Kitzmann & Joachim Stock

EVolve calculates the chemical composition and surface pressure of a ID atmosphere on a rocky planet which is being produced by volcanic activity, as it grows over time. Once the initial volatile content of the planet's mantle, and the composition & resultant surface pressure of any pre-existing atmosphere is set, a volcanic degassing model (EVo) will calculate the amount and speciation of any volcanic gases released into the atmosphere over each time step. Thermochemical equilibrium is assumed so the final chemical composition of the atmosphere is calculated according to the pre-set surface temperature. Future versions will include hydrogen escape as a loss mechanism.

Code Language(s): Python3, C++

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EVolve: Growth and evolution of volcanically-derived atmospheres

Philippa Liggins, incorporating the FastChem 2.0 model of Daniel Kitzmann & Joachim Stock

EVolve calculates the chemical composition and surface pressure of a ID atmosphere on a rocky planet which is being produced by volcanic activity, as it grows over time. Once the initial volatile content of the planet's mantle, and the composition & resultant surface pressure of any pre-existing atmosphere is set, a volcanic degassing model (EVo) will calculate the amount and speciation of any volcanic gases released into the atmosphere over each time step. Thermochemical equilibrium is assumed so the final chemical composition of the atmosphere is calculated according to the pre-set surface temperature. Future versions will include hydrogen escape as a loss mechanism.

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gCMCRT: A GPU accelerated MCRT code for (exo)planetary atmospheres

Elspeth K. H. Lee

gCMCRT (gpu Cloudy Monte Carlo Radiative Transfer) is a 3D Monte Carlo Radiative-Transfer (MCRT) and ray-tracing hybrid code suitable for a wide variety of synthetic spectra modeling for (exo)planetary atmospheres, using GPU hardware to accelerate the RT calculation. Primarily aimed at post-processing 1D global averaged or 3D GCM model output, gCMCRT can calculate albedo, emission and transmission spectra as well as phase curves from model outputs. gCMCRT has functionality to model high-resolution spectra including doppler shifting effects. gCMCRT also contains an opacity mixer/interpolator (optools) as well as a Mie theory solver to help produce the opacity structures of the atmosphere.

Code Language(s): CUDA Fortran

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gCMCRT: A GPU accelerated MCRT code for (exo)planetary atmospheres

Elspeth K. H. Lee

gCMCRT (gpu Cloudy Monte Carlo Radiative Transfer) is a 3D Monte Carlo Radiative-Transfer (MCRT) and ray-tracing hybrid code suitable for a wide variety of synthetic spectra modeling for (exo)planetary atmospheres, using GPU hardware to accelerate the RT calculation. Primarily aimed at post-processing 1D global averaged or 3D GCM model output, gCMCRT can calculate albedo, emission and transmission spectra as well as phase curves from model outputs. gCMCRT has functionality to model high-resolution spectra including doppler shifting effects. gCMCRT also contains an opacity mixer/interpolator (optools) as well as a Mie theory solver to help produce the opacity structures of the atmosphere.

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Exodetbox: Underlying Methods for Calculating Integration Time Adjusted Completeness

Dean Keithly

This code repository contains methods for quickly determining the apparent planet-star separation extrema and difference in magnitude extrema of any given Keplerian orbital elements. It additionally contains methods for calculating when along the planet's orbit it has a specific planet-star separation and specific difference in magnitude. Using a coronagraph, or starshade's, inner working angle, outer working angle, and photometric limit of integration, planet visibility windows can be calculated and tabulated to compute an Integration Time Adjusted Completeness. Methods implemented in this code is presented in Keithly, Savransky, "Integration Time Adjusted Completeness", JATIS, 2021.

Code Language(s): Python3

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Exodetbox: Underlying Methods for Calculating Integration Time Adjusted Completeness

Dean Keithly

This code repository contains methods for quickly determining the apparent planet-star separation extrema and difference in magnitude extrema of any given Keplerian orbital elements. It additionally contains methods for calculating when along the planet's orbit it has a specific planet-star separation and specific difference in magnitude. Using a coronagraph, or starshade's, inner working angle, outer working angle, and photometric limit of integration, planet visibility windows can be calculated and tabulated to compute an Integration Time Adjusted Completeness. Methods implemented in this code is presented in Keithly, Savransky, "Integration Time Adjusted Completeness", JATIS, 2021.

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ThERESA: Three-Dimensional Eclipse Mapping with Spectroscopic Lightcurves

Ryan C. Challener, Emily Rauscher

ThERESA is a 3D exoplanet atmospheric retrieval package. ThERESA individually fits 2D temperature maps for each lightcurve in a spectroscopic eclipse (or phase curve) observation using maximally-informative "eigencurves." It then places these 2D maps in 3D space, using a variety of models, to retrieve the planet's 3D temperature structure. ThERESA then calculates thermochemical equilibrium abundances and emission across the planet, which is then integrated spectrally and spatially to compare with all lightcurves simultaneously. This is repeated behind MCMC to obtain accurate parameter uncertainty estimates. Analyses can take a few days to a few weeks, depending on model complexity.

Code Language(s): Python3

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ThERESA: Three-Dimensional Eclipse Mapping with Spectroscopic Lightcurves

Ryan C. Challener, Emily Rauscher

ThERESA is a 3D exoplanet atmospheric retrieval package. ThERESA individually fits 2D temperature maps for each lightcurve in a spectroscopic eclipse (or phase curve) observation using maximally-informative "eigencurves." It then places these 2D maps in 3D space, using a variety of models, to retrieve the planet's 3D temperature structure. ThERESA then calculates thermochemical equilibrium abundances and emission across the planet, which is then integrated spectrally and spatially to compare with all lightcurves simultaneously. This is repeated behind MCMC to obtain accurate parameter uncertainty estimates. Analyses can take a few days to a few weeks, depending on model complexity.

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Exo-REM: 1D self-consistent radiative-equilibrium model for exoplanetary atmospheres

Baudino, J.-L.; Bézard, B.; Charnay, B. and Blain, D.

Exo-REM is a 1D radiative-equilibrium model developed for the simulation of the atmosphere of H2-dominated exoplanetary atmospheres. Fluxes are calculated using the two-stream approximation. The radiative-convective equilibrium is solved assuming that the net flux (radiative + convective) is conservative. The conservation of flux over the pressure grid is solved iteratively using a constrained linear inversion method. Rayleigh scattering as well as absorption and scattering by clouds (calculated from extinction coefficient, single scattering albedo, and asymmetry factor interpolated from precomputed tables for a set of wavelengths and particle radii) are also taken into account.

Code Language(s): Python3, Fortran

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Exo-REM: 1D self-consistent radiative-equilibrium model for exoplanetary atmospheres

Baudino, J.-L.; Bézard, B.; Charnay, B. and Blain, D.

Exo-REM is a 1D radiative-equilibrium model developed for the simulation of the atmosphere of H2-dominated exoplanetary atmospheres. Fluxes are calculated using the two-stream approximation. The radiative-convective equilibrium is solved assuming that the net flux (radiative + convective) is conservative. The conservation of flux over the pressure grid is solved iteratively using a constrained linear inversion method. Rayleigh scattering as well as absorption and scattering by clouds (calculated from extinction coefficient, single scattering albedo, and asymmetry factor interpolated from precomputed tables for a set of wavelengths and particle radii) are also taken into account.

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UBER: Universal Boltzmann Equation Solver

Zheng, L. et al.

UBER is a Fortran library that solves the general form of Fokker-Planck equation and Boltzmann equation, diffusive or non-diffusive, that appear in modeling planetary radiation belts. Users can freely specify (1) the coordinate system, (2) boundary geometry and boundary conditions, and (3) the equation terms and coefficients. The solver works for problems in one to three spatial dimensions. The solver is based upon the mathematical theory of stochastic differential equations which is of Monte Carlo nature, and the solution stochastic uncertainty may be dictated arbitrarily small at the cost of longer iterations.

Code Language(s): C, Fortran, MATLAB

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UBER: Universal Boltzmann Equation Solver

Zheng, L. et al.

UBER is a Fortran library that solves the general form of Fokker-Planck equation and Boltzmann equation, diffusive or non-diffusive, that appear in modeling planetary radiation belts. Users can freely specify (1) the coordinate system, (2) boundary geometry and boundary conditions, and (3) the equation terms and coefficients. The solver works for problems in one to three spatial dimensions. The solver is based upon the mathematical theory of stochastic differential equations which is of Monte Carlo nature, and the solution stochastic uncertainty may be dictated arbitrarily small at the cost of longer iterations.

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zeus: A Python implementation of ensemble slice sampling for efficient Bayesian parameter inference

M. Karamanis, F. Beutler, J.A. Peacock

We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov chain Monte Carlo (MCMC) algorithm specifically designed to tackle the computational challenges posed by modern astronomical and cosmological analyses. In particular, the method requires only minimal hand--tuning of 1-2 hyper-parameters that are often trivial to set; its performance is insensitive to linear correlations and it can scale up to 1000s of CPUs without any extra effort. Furthermore, its locally adaptive nature allows to sample efficiently even when strong non-linear correlations are present.

Code Language(s): Python3

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zeus: A Python implementation of ensemble slice sampling for efficient Bayesian parameter inference

M. Karamanis, F. Beutler, J.A. Peacock

We introduce zeus, a well-tested Python implementation of the Ensemble Slice Sampling (ESS) method for Bayesian parameter inference. ESS is a novel Markov chain Monte Carlo (MCMC) algorithm specifically designed to tackle the computational challenges posed by modern astronomical and cosmological analyses. In particular, the method requires only minimal hand--tuning of 1-2 hyper-parameters that are often trivial to set; its performance is insensitive to linear correlations and it can scale up to 1000s of CPUs without any extra effort. Furthermore, its locally adaptive nature allows to sample efficiently even when strong non-linear correlations are present.

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The all-sky PLATO input catalogue

Montalto et al. 2021, A&A, 653, 98

The ESA PLAnetary Transits and Oscillations of stars (PLATO) mission will search for terrestrial planets in the habitable zone of solar-type stars. Because of telemetry limitations, PLATO targets need to be pre-selected. We present an all sky catalog that will be fundamental to select the best PLATO fields and the most promising target stars, derive their fundamental parameters, analyze the instrumental performances, and then plan and optimize follow-up observations. This catalog also represents a valuable resource for the general definition of stellar samples optimized for the search of transiting planets.

Code Language(s): N/A

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The all-sky PLATO input catalogue

Montalto et al. 2021, A&A, 653, 98

The ESA PLAnetary Transits and Oscillations of stars (PLATO) mission will search for terrestrial planets in the habitable zone of solar-type stars. Because of telemetry limitations, PLATO targets need to be pre-selected. We present an all sky catalog that will be fundamental to select the best PLATO fields and the most promising target stars, derive their fundamental parameters, analyze the instrumental performances, and then plan and optimize follow-up observations. This catalog also represents a valuable resource for the general definition of stellar samples optimized for the search of transiting planets.

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The Photometry Pipeline (PP): Automated photometry pipeline for small to medium-sized observatories

Michael Mommert

The Photometry Pipeline (PP) is a Python 3 software package for automated photometric analysis of imaging data from small to medium-sized observatories. It uses Source Extractor and SCAMP to register and photometrically calibrate images based on catalogs that are available online; photometry is measured using Source Extractor aperture photometry. PP has been designed for asteroid observations, but can be used with any kind of imaging data. (No longer maintained)

Code Language(s): Python3

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The Photometry Pipeline (PP): Automated photometry pipeline for small to medium-sized observatories

Michael Mommert

The Photometry Pipeline (PP) is a Python 3 software package for automated photometric analysis of imaging data from small to medium-sized observatories. It uses Source Extractor and SCAMP to register and photometrically calibrate images based on catalogs that are available online; photometry is measured using Source Extractor aperture photometry. PP has been designed for asteroid observations, but can be used with any kind of imaging data. (No longer maintained)

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ExoInt: A devolatilization and interior modeling package for rocky planets

Haiyang S. Wang

Purpose: devolatilize stellar abundances to produce rocky exoplanetary bulk composition, with which constraining the modeling of the exoplanet interiors. A moderate updated version (v1.2) is available under the folder "v1.2". The codes are currently written in IDL, and a Python version has been developed and will be online soon. Please refer to 'About' for how to run the codes. For questions, comments, and suggestions, please raise them in the 'Issues' tab or otherwise directly send to Haiyang Wang at haiwang@phys.ethz.ch (for requests, in particular).

Code Language(s): IDL

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ExoInt: A devolatilization and interior modeling package for rocky planets

Haiyang S. Wang

Purpose: devolatilize stellar abundances to produce rocky exoplanetary bulk composition, with which constraining the modeling of the exoplanet interiors. A moderate updated version (v1.2) is available under the folder "v1.2". The codes are currently written in IDL, and a Python version has been developed and will be online soon. Please refer to 'About' for how to run the codes. For questions, comments, and suggestions, please raise them in the 'Issues' tab or otherwise directly send to Haiyang Wang at haiwang@phys.ethz.ch (for requests, in particular).

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TRAN_K2: Search for planetary transits embedded in stellar variability and systematic effects

Geza Kovacs

TRAN_K2 is standalone Fortran code to search for planetary transits under the colored noise of stellar variability and instrumental effects. Stellar variability is represented by a Fourier series and, when necessary, by an autoregressive model aimed at avoiding excessive Gibbs overshoots at the edges. For the treatment of systematics, a co-trending and an external parameter decorrelation are employed. The filtering is done within the framework of the standard weighted least squares, where the weights are determined iteratively, to allow a robust fit and to separate the transit signal from stellar variability and systematics.

Code Language(s): Fortran, Shell

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TRAN_K2: Search for planetary transits embedded in stellar variability and systematic effects

Geza Kovacs

TRAN_K2 is standalone Fortran code to search for planetary transits under the colored noise of stellar variability and instrumental effects. Stellar variability is represented by a Fourier series and, when necessary, by an autoregressive model aimed at avoiding excessive Gibbs overshoots at the edges. For the treatment of systematics, a co-trending and an external parameter decorrelation are employed. The filtering is done within the framework of the standard weighted least squares, where the weights are determined iteratively, to allow a robust fit and to separate the transit signal from stellar variability and systematics.

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PlanetPack: Command-line Tool for Radial Velocity and Transit Lightcurves Fitting

R.V. Baluev

PlanetPack is a software tool developed to facilitate the radial-velocity and transit data analysis for the goal of exoplanets detection, characterization, and basic dynamical simulations. The description of the main theoretic concepts, statistical methods and algorithms that PlanetPack implements, is given in the following refereed papers: R.V. Baluev 2013, Astronomy & Computing, V. 2, P. 18 (initial release); R.V. Baluev 2018, Astronomy & Computing, V. 25, P. 221 (update 3.0).

Code Language(s): C++

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PlanetPack: Command-line Tool for Radial Velocity and Transit Lightcurves Fitting

R.V. Baluev

PlanetPack is a software tool developed to facilitate the radial-velocity and transit data analysis for the goal of exoplanets detection, characterization, and basic dynamical simulations. The description of the main theoretic concepts, statistical methods and algorithms that PlanetPack implements, is given in the following refereed papers: R.V. Baluev 2013, Astronomy & Computing, V. 2, P. 18 (initial release); R.V. Baluev 2018, Astronomy & Computing, V. 25, P. 221 (update 3.0).

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SMINT: Structure Model INTerpolator

Caroline Piaulet, with models from several published papers (see documentation)

Using mass-radius grids for water- or hydrogen-enveloped small planets, astronomers can infer the range of plausible compositions that a planet may have based on its mass, radius, age and/or level of insolation. In its present form, smint presents a tool to leverage in this way several published mass-radius grids in a Bayesian framework. The interface is user-friendly: the user can input the parameters of the planet of interest with specifications on the priors that should be used, and the tool returns publication-ready plots presenting the joint parameters constraints obtained from interpolating the interior models grid of interest as well as confidence intervals for each parameter.

Code Language(s): Python3

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SMINT: Structure Model INTerpolator

Caroline Piaulet, with models from several published papers (see documentation)

Using mass-radius grids for water- or hydrogen-enveloped small planets, astronomers can infer the range of plausible compositions that a planet may have based on its mass, radius, age and/or level of insolation. In its present form, smint presents a tool to leverage in this way several published mass-radius grids in a Bayesian framework. The interface is user-friendly: the user can input the parameters of the planet of interest with specifications on the priors that should be used, and the tool returns publication-ready plots presenting the joint parameters constraints obtained from interpolating the interior models grid of interest as well as confidence intervals for each parameter.

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TRIPPy: Python based Trailed Source Photometry

Wesley C. Fraser

TRIPPy is a python package aimed to perform all the steps required to measure accurate photometry of both trailed and non-trailed (stationary) astronomical sources. This includes the ability to generate stellar and trailed point source functions, and to use circular and pill shaped apertures to measure photometry and estimate appropriate aperture corrections. Tools for source fitting with a model PSF (both MCMC and classical least-squares minimizers) are available. Citation: If you use TRIPPy in your science works, please cite Fraser, W. et al., 2016, Astronomical Journal, 151. DOI at Zenodo.

Code Language(s): Python2, Python3

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TRIPPy: Python based Trailed Source Photometry

Wesley C. Fraser

TRIPPy is a python package aimed to perform all the steps required to measure accurate photometry of both trailed and non-trailed (stationary) astronomical sources. This includes the ability to generate stellar and trailed point source functions, and to use circular and pill shaped apertures to measure photometry and estimate appropriate aperture corrections. Tools for source fitting with a model PSF (both MCMC and classical least-squares minimizers) are available. Citation: If you use TRIPPy in your science works, please cite Fraser, W. et al., 2016, Astronomical Journal, 151. DOI at Zenodo.

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PEP: The Planetary Ephemeris Program

Developed by many; original: Michael Ash; most recent and longest- time-involved: John Chandler

This Fortran computer program models orbital motion in the solar system, including almost 100 individual asteroids as well as all of the planets and some moons, along with a detailed model of our moon, and a model of pulsar motions and of distant radio sources. It takes as input diverse astrometric data: radio, radar, laser, timing of signal arrivals, and VLBI. The program can solve for well over 100 parameters, including orbital and (for some bodies) rotational initial conditions, sky coordinates for radio sources, plasma densities, the second harmonic of the Sun's gravitational field, and those related to tests of fundamental physics.

Code Language(s): Fortran

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PEP: The Planetary Ephemeris Program

Developed by many; original: Michael Ash; most recent and longest- time-involved: John Chandler

This Fortran computer program models orbital motion in the solar system, including almost 100 individual asteroids as well as all of the planets and some moons, along with a detailed model of our moon, and a model of pulsar motions and of distant radio sources. It takes as input diverse astrometric data: radio, radar, laser, timing of signal arrivals, and VLBI. The program can solve for well over 100 parameters, including orbital and (for some bodies) rotational initial conditions, sky coordinates for radio sources, plasma densities, the second harmonic of the Sun's gravitational field, and those related to tests of fundamental physics.

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DYNAMITE: DYNAmical Multi-planet [System Architecture] Injection [via Monte Carlo Integrations] TEster

Dietrich, J., Apai, D., et al.

We start with the specific (yet often incomplete) data on the orbital and physical parameters for the planets in any given system's architecture. We combine that with detailed statistical population models and a dynamical stability criterion to predict the likelihood for the parameters of one additional planet in the system. These predictions are given in the form of observable values (transit depth measurements, RV semi-amplitudes, or direct imaging separation and contrast) that can be tested by follow-up observations. This work was done as a member of the Earths in Other Solar Systems and Alien Earths projects, funded by NASA with grant nos. 3013511 and 80NSSC21K0593

Code Language(s): Python3

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DYNAMITE: DYNAmical Multi-planet [System Architecture] Injection [via Monte Carlo Integrations] TEster

Dietrich, J., Apai, D., et al.

We start with the specific (yet often incomplete) data on the orbital and physical parameters for the planets in any given system's architecture. We combine that with detailed statistical population models and a dynamical stability criterion to predict the likelihood for the parameters of one additional planet in the system. These predictions are given in the form of observable values (transit depth measurements, RV semi-amplitudes, or direct imaging separation and contrast) that can be tested by follow-up observations. This work was done as a member of the Earths in Other Solar Systems and Alien Earths projects, funded by NASA with grant nos. 3013511 and 80NSSC21K0593

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VULCAN: Photochemical kinetics for planetary atmospheres

Shang-Min (Shami) Tsai

Photochemical kinetics for (exo-)planetary atmospheres, a fast and easy-to-use python code. The model has hierarchical C-H-N-O-S networks and treats thermochemistry, photochemistry, eddy diffusion, advection transport, condensation, and various boundary conditions.

Code Language(s): C++, Python2, Python3

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VULCAN: Photochemical kinetics for planetary atmospheres

Shang-Min (Shami) Tsai

Photochemical kinetics for (exo-)planetary atmospheres, a fast and easy-to-use python code. The model has hierarchical C-H-N-O-S networks and treats thermochemistry, photochemistry, eddy diffusion, advection transport, condensation, and various boundary conditions.

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APOLLO: MCMC Exoplanet Atmosphere Retrieval Code

Alex Howe & Arthur Adams

APOLLO is an exoplanet atmosphere retrieval code designed for flexibility and comparison of models. The code computes 1-D forward models of exoplanet spectrum in transit or emission and fits them to observations using an MCMC method. APOLLO includes options for multiple radiative transfer algorithms, temperature-pressure profiles, and cloud parameterizations, allowing for comparison of models using different physics prescriptions. APOLLO can also generate synthetic spectra in the JWST spectroscopic modes, as well as compute photometric fluxes.

Code Language(s): C++, Python3

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APOLLO: MCMC Exoplanet Atmosphere Retrieval Code

Alex Howe & Arthur Adams

APOLLO is an exoplanet atmosphere retrieval code designed for flexibility and comparison of models. The code computes 1-D forward models of exoplanet spectrum in transit or emission and fits them to observations using an MCMC method. APOLLO includes options for multiple radiative transfer algorithms, temperature-pressure profiles, and cloud parameterizations, allowing for comparison of models using different physics prescriptions. APOLLO can also generate synthetic spectra in the JWST spectroscopic modes, as well as compute photometric fluxes.

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IcyDwarf: IcyDwarf computes the coupled geophysical-chemical-orbital evolution of an icy dwarf planet or moon

Neveu, M. from a geophysical evolution code initially developed by Desch, S. et al. (2009)

IcyDwarf calculates:

  • The thermal evolution of one or more icy moon(s) or dwarf planet(s), with no chemistry, but with rock hydration, dehydration, hydrothermal circulation, core cracking, tidal heating, and porosity. The depth of cracking and a bulk water:rock ratio by mass in the rocky core are also computed, as well as moon orbital evolution.
  • Whether cryovolcanism is possible by the exsolution of volatiles from cryolavas.
  • Equilibrium fluid and rock chemistries resulting from water-rock interaction in subsurface oceans in contact with a rocky core, up to 200ºC and 1000 bar

Code Language(s): C

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IcyDwarf: IcyDwarf computes the coupled geophysical-chemical-orbital evolution of an icy dwarf planet or moon

Neveu, M. from a geophysical evolution code initially developed by Desch, S. et al. (2009)

IcyDwarf calculates:

  • The thermal evolution of one or more icy moon(s) or dwarf planet(s), with no chemistry, but with rock hydration, dehydration, hydrothermal circulation, core cracking, tidal heating, and porosity. The depth of cracking and a bulk water:rock ratio by mass in the rocky core are also computed, as well as moon orbital evolution.
  • Whether cryovolcanism is possible by the exsolution of volatiles from cryolavas.
  • Equilibrium fluid and rock chemistries resulting from water-rock interaction in subsurface oceans in contact with a rocky core, up to 200ºC and 1000 bar

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Deep-Transit: Identify Transit Signals with Deep Learning Based Object Detection Algorithm

Cui, K. et al.

Deep-Transit is an open-source Python package designed for transit detection with a deep learning based 2D object detection algorithm. For simple usage, Deep-Transit can handle your light curve and then output the transiting candidates' bounding boxes and confidence scores. Deep-Transit has already been trained for Kepler and TESS data, but can be easily extended to other photometric surveys, even ground-based observations. Deep-Transit also provides the interface to train on your own datasets.

Code Language(s): Python3

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Deep-Transit: Identify Transit Signals with Deep Learning Based Object Detection Algorithm

Cui, K. et al.

Deep-Transit is an open-source Python package designed for transit detection with a deep learning based 2D object detection algorithm. For simple usage, Deep-Transit can handle your light curve and then output the transiting candidates' bounding boxes and confidence scores. Deep-Transit has already been trained for Kepler and TESS data, but can be easily extended to other photometric surveys, even ground-based observations. Deep-Transit also provides the interface to train on your own datasets.

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ATES: ATmospheric EScape

The ATES hydrodynamics code computes the temperature, density, velocity and ionization fraction profiles of highly irradiated planetary atmospheres, along with the current, steady-state mass loss rate. ATES solves the one-dimensional Euler, mass and energy conservation equations in radial coordinates through a finite-volume scheme. The hydrodynamics module is paired with a photoionization equilibrium solver that includes cooling via bremsstrahlung, recombination and collisional excitation/ionization for the case of an atmosphere of primordial composition (i.e., pure atomic hydrogen-helium), while also accounting for advection of the different ion species.

Code Language(s): Fortran, Python3

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ATES: ATmospheric EScape

The ATES hydrodynamics code computes the temperature, density, velocity and ionization fraction profiles of highly irradiated planetary atmospheres, along with the current, steady-state mass loss rate. ATES solves the one-dimensional Euler, mass and energy conservation equations in radial coordinates through a finite-volume scheme. The hydrodynamics module is paired with a photoionization equilibrium solver that includes cooling via bremsstrahlung, recombination and collisional excitation/ionization for the case of an atmosphere of primordial composition (i.e., pure atomic hydrogen-helium), while also accounting for advection of the different ion species.

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EXOTIC (EXOplanet Transit Interpretation Code): A Python3 package for analyzing photometric data of transiting exoplanets

The Exoplanet Watch Team

A Python 3 package for analyzing photometric data of transiting exoplanets into lightcurves and retrieving transit epochs and planetary radii. EXOTIC can run on a Windows, Macintosh, or Linux/Unix computer. You can also use EXOTIC via the free Google Colab, which features cloud computing, many helpful plotting functions, and a simplified installation. However, if you are a user with many images or large images, we recommend running EXOTIC locally on your own computer.

Code Language(s): Python3

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EXOTIC (EXOplanet Transit Interpretation Code): A Python3 package for analyzing photometric data of transiting exoplanets

The Exoplanet Watch Team

A Python 3 package for analyzing photometric data of transiting exoplanets into lightcurves and retrieving transit epochs and planetary radii. EXOTIC can run on a Windows, Macintosh, or Linux/Unix computer. You can also use EXOTIC via the free Google Colab, which features cloud computing, many helpful plotting functions, and a simplified installation. However, if you are a user with many images or large images, we recommend running EXOTIC locally on your own computer.

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GRIT: A package for structure-preserving simulations of gravitationally interacting rigid-bodies

Chen, Renyi; Li, Gongjie and Tao, Molei

This is a package dedicated to the simulation of N gravitationally interacting rigid/point mass bodies. Tidal forces and general relativity correction are supported. Multiscale splittings are included to boost the simulation speed. Multiple schemes with different orders of convergences and splitting strategies are available. Force evaluations can be parallelized. Floating-point format can be customized as float / double / long double globally.

Code Language(s): C++

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GRIT: A package for structure-preserving simulations of gravitationally interacting rigid-bodies

Chen, Renyi; Li, Gongjie and Tao, Molei

This is a package dedicated to the simulation of N gravitationally interacting rigid/point mass bodies. Tidal forces and general relativity correction are supported. Multiscale splittings are included to boost the simulation speed. Multiple schemes with different orders of convergences and splitting strategies are available. Force evaluations can be parallelized. Floating-point format can be customized as float / double / long double globally.

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ExoPlaSim: Extending the Planet Simulator for Exoplanets

Paradise, A. et al.

A modified version of the PlaSim 3D climate model, designed to simulate planets with Earth-like atmospheric compositions across a wide parameter space, including tidally-locked rotation, 0.1-10 bars surface pressure, and a range of stellar spectra. ExoPlaSim has a Python API for configuring and running models, as well as utilities for interacting with and analyzing the netCDF output files. ExoPlaSim is also pip-installable. As an intermediate-complexity model, ExoPlaSim trades some complexity for speed, and is able to run on a range of hardware including personal laptops and high-performance computing clusters, with typical performance of 1 year of climate at T21 resolution in 1-5 minutes.

Code Language(s): C, Fortran, Python3

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ExoPlaSim: Extending the Planet Simulator for Exoplanets

Paradise, A. et al.

A modified version of the PlaSim 3D climate model, designed to simulate planets with Earth-like atmospheric compositions across a wide parameter space, including tidally-locked rotation, 0.1-10 bars surface pressure, and a range of stellar spectra. ExoPlaSim has a Python API for configuring and running models, as well as utilities for interacting with and analyzing the netCDF output files. ExoPlaSim is also pip-installable. As an intermediate-complexity model, ExoPlaSim trades some complexity for speed, and is able to run on a range of hardware including personal laptops and high-performance computing clusters, with typical performance of 1 year of climate at T21 resolution in 1-5 minutes.

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Hypatia Catalog: Database of stellar elemental abundances for solar neighborhood stars

Hinkel, N.R. et al. (2014)

The Hypatia Catalog is a comprehensive collection of literature data and the largest database of stellar abundances for stars near to the Sun (Hinkel et al. 2014}. The multidimensional database currently spans 78 unique elements and species in ~9400 stars, ~1300 of which are planet hosts, within 500 pc of the Sun, including all exoplanet host stars regardless of distance. Hypatia was compiled from ~215 literature source abundance measurements that were re-normalized to the same solar scale, so that all values were on a common baseline.

Code Language(s): N/A

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Hypatia Catalog: Database of stellar elemental abundances for solar neighborhood stars

Hinkel, N.R. et al. (2014)

The Hypatia Catalog is a comprehensive collection of literature data and the largest database of stellar abundances for stars near to the Sun (Hinkel et al. 2014}. The multidimensional database currently spans 78 unique elements and species in ~9400 stars, ~1300 of which are planet hosts, within 500 pc of the Sun, including all exoplanet host stars regardless of distance. Hypatia was compiled from ~215 literature source abundance measurements that were re-normalized to the same solar scale, so that all values were on a common baseline.

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KOBE: Kepler Observes Bern Exoplanets

Lokesh Mishra

KOBE adds the geometrical limitations and the physical detection biases of the transit method to a given population of theoretical planets. In addition, it also adds the completeness and reliability of a transit survey. For more details, click here.

Code Language(s): Python3

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KOBE: Kepler Observes Bern Exoplanets

Lokesh Mishra

KOBE adds the geometrical limitations and the physical detection biases of the transit method to a given population of theoretical planets. In addition, it also adds the completeness and reliability of a transit survey. For more details, click here.

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Pyratbay: A Forward-modeling and retrieval code to model exoplanet atmospheres and spectra

Cubillos, P. E. and Blecic, J.

The Pyrat Bay framework is an open-source pack for exoplanet atmospheric modeling, spectral synthesis, and Bayesian retrieval. The modular design of the code allows the users to generate atmospheric 1D parametric models of the temperature, abundances (equilibrium or constant profiles), and altitude profiles in hydrostatic equilibrium; sample ExoMol and HITRAN line-by-line cross sections with custom resolving power and line-wing cutoff values; compute emission or transmission spectra considering cross sections from molecular line transitions, collision-induced absorption, Rayleigh scattering, gray clouds, and alkali resonance lines; and perform Markov chain Monte Carlo atmospheric retrievals.

Code Language(s): Python3

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Pyratbay: A Forward-modeling and retrieval code to model exoplanet atmospheres and spectra

Cubillos, P. E. and Blecic, J.

The Pyrat Bay framework is an open-source pack for exoplanet atmospheric modeling, spectral synthesis, and Bayesian retrieval. The modular design of the code allows the users to generate atmospheric 1D parametric models of the temperature, abundances (equilibrium or constant profiles), and altitude profiles in hydrostatic equilibrium; sample ExoMol and HITRAN line-by-line cross sections with custom resolving power and line-wing cutoff values; compute emission or transmission spectra considering cross sections from molecular line transitions, collision-induced absorption, Rayleigh scattering, gray clouds, and alkali resonance lines; and perform Markov chain Monte Carlo atmospheric retrievals.

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SparseBLS: Box-Fitting Least Squares implementation for sparse data

Aviad Panahi, Shay Zucker

A new implementation of the commonly used Box-fitting Least Squares (BLS) algorithm, for the detection of transiting exoplanets in photometric data. Unlike BLS, our new implementation—Sparse BLS, does not use binning of the data into phase bins, nor does it use any kind of phase grid. Thus, its detection efficiency does not depend on the transit phase, and is therefore slightly better than that of BLS. For sparse data, it is also significantly faster than BLS. It is therefore perfectly suitable for large photometric surveys producing unevenly-sampled sparse light curves, such as Gaia.

Code Language(s): Java8

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SparseBLS: Box-Fitting Least Squares implementation for sparse data

Aviad Panahi, Shay Zucker

A new implementation of the commonly used Box-fitting Least Squares (BLS) algorithm, for the detection of transiting exoplanets in photometric data. Unlike BLS, our new implementation—Sparse BLS, does not use binning of the data into phase bins, nor does it use any kind of phase grid. Thus, its detection efficiency does not depend on the transit phase, and is therefore slightly better than that of BLS. For sparse data, it is also significantly faster than BLS. It is therefore perfectly suitable for large photometric surveys producing unevenly-sampled sparse light curves, such as Gaia.

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RadVel: Radial Velocity Fitting Toolkit: General Toolkit for Modeling Radial Velocities

BJ Fulton, Erik Petigura, Sarah Blunt, and Evan Sinukoff

RadVel is a tool designed to fit Keplerian orbits to radial velocity datasets. Multiple planets, multiple instruments, and multiple sources of white and red noise are supported out of the box. RadVel is written in an extensible object-oriented framework which allows for custom models, priors, or samplers.

Code Language(s): Python2

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RadVel: Radial Velocity Fitting Toolkit: General Toolkit for Modeling Radial Velocities

BJ Fulton, Erik Petigura, Sarah Blunt, and Evan Sinukoff

RadVel is a tool designed to fit Keplerian orbits to radial velocity datasets. Multiple planets, multiple instruments, and multiple sources of white and red noise are supported out of the box. RadVel is written in an extensible object-oriented framework which allows for custom models, priors, or samplers.

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SpaceHub: A high-performance gravity integration toolkit for few-body problems in astrophysics

Yihan Wang, Nathan Leigh, Bin Liu and Rosalba Perna

SpaceHub uses unique algorithms for fast, precise and accurate computations for few-body problems, ranging from interacting black holes to planetary dynamics. This few-body gravity integration toolkit can treat black hole dynamics with extreme mass ratios, extreme eccentricities and very close encounters. SpaceHub offers a bulk of regularized integrator and other cutting edge few body methods and can handle extremely eccentric orbits and close approaches in long-term integrations.

Code Language(s): C++

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SpaceHub: A high-performance gravity integration toolkit for few-body problems in astrophysics

Yihan Wang, Nathan Leigh, Bin Liu and Rosalba Perna

SpaceHub uses unique algorithms for fast, precise and accurate computations for few-body problems, ranging from interacting black holes to planetary dynamics. This few-body gravity integration toolkit can treat black hole dynamics with extreme mass ratios, extreme eccentricities and very close encounters. SpaceHub offers a bulk of regularized integrator and other cutting edge few body methods and can handle extremely eccentric orbits and close approaches in long-term integrations.

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https://emac.gsfc.nasa.gov?related_resource=d430aa23-4db8-4f3d-8558-67d110bdac2d
Astronet-Triage: A Neural Network for TESS Light Curve Triage

Yu, L. et al.

Astronet-Triage is a deep learning model capable of performing triage on TESS candidates. It is trained and tested on real TESS light curves and can distinguish transit-like signals (planet candidates and eclipsing binaries) from stellar variability and instrumental noise with an average precision of 97.0% and an accuracy of 97.4%. For the vetting version of this model, see Astronet Vetting.

Code Language(s): Python3

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Astronet-Triage: A Neural Network for TESS Light Curve Triage

Yu, L. et al.

Astronet-Triage is a deep learning model capable of performing triage on TESS candidates. It is trained and tested on real TESS light curves and can distinguish transit-like signals (planet candidates and eclipsing binaries) from stellar variability and instrumental noise with an average precision of 97.0% and an accuracy of 97.4%. For the vetting version of this model, see Astronet Vetting.

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FALCO: Wavefront Estimation and Control Software for Coronagraphs

Riggs, A J Eldorado

The Fast Linearized Coronagraph Optimizer (FALCO) is an open-source, modular software collection for the design, modeling, or testbed operation of coronagraphic optical systems. Users can build PROPER models of their optical systems and then use FALCO as a wrapper for performing wavefront correction simulations. FALCO contains working examples of published wavefront estimation and control techniques. The software can be easily ported to different testbeds and is already being used to run benches at Caltech and JPL.

Code Language(s): MATLAB, Python3

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https://emac.gsfc.nasa.gov?related_resource=e430c5de-b542-4a2f-ace9-00333a3f03a4

FALCO: Wavefront Estimation and Control Software for Coronagraphs

Riggs, A J Eldorado

The Fast Linearized Coronagraph Optimizer (FALCO) is an open-source, modular software collection for the design, modeling, or testbed operation of coronagraphic optical systems. Users can build PROPER models of their optical systems and then use FALCO as a wrapper for performing wavefront correction simulations. FALCO contains working examples of published wavefront estimation and control techniques. The software can be easily ported to different testbeds and is already being used to run benches at Caltech and JPL.

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exoscene: A Python library for simulating direct images of exoplanetary systems

Zimmerman, Neil

exoscene is a library of classes and utility functions for simulating direct images of exoplanetary systems. The package was developed by Neil Zimmerman (NASA/GSFC), with source code contributions from Maxime Rizzo, Christopher Stark, and Ell Bogat. This work was funded in part by a WFIRST/Roman Science Investigation Team contract (PI: Margaret Turnbull).

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?related_resource=820ff968-92a2-4d8b-93ac-297978b2599a

exoscene: A Python library for simulating direct images of exoplanetary systems

Zimmerman, Neil

exoscene is a library of classes and utility functions for simulating direct images of exoplanetary systems. The package was developed by Neil Zimmerman (NASA/GSFC), with source code contributions from Maxime Rizzo, Christopher Stark, and Ell Bogat. This work was funded in part by a WFIRST/Roman Science Investigation Team contract (PI: Margaret Turnbull).

About Demo
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ESO SkyCalc: Web and API interface to The Cerro Paranal Advanced Sky Model

Noll et al. (2012, A&A 543, A92) and Jones et al. (2013, A&A 560, A91)

SkyCalc is a web application based on the Cerro Paranal Advanced Sky Model, which was developed in particular to be used in the ESO Exposure Time Calculators, by a team of astronomers at the Institute for Astro- and Particle Physics at the University of Innsbruck, as part of an Austrian in-kind contribution to ESO. A command-line tool skycalc_cli is available to execute the SkyCalc backend engine through an API. The C source code is available to download.

Code Language(s): C, Shell

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https://emac.gsfc.nasa.gov?related_resource=f0789a22-abf3-467e-8d74-503d14e677df

ESO SkyCalc: Web and API interface to The Cerro Paranal Advanced Sky Model

Noll et al. (2012, A&A 543, A92) and Jones et al. (2013, A&A 560, A91)

SkyCalc is a web application based on the Cerro Paranal Advanced Sky Model, which was developed in particular to be used in the ESO Exposure Time Calculators, by a team of astronomers at the Institute for Astro- and Particle Physics at the University of Innsbruck, as part of an Austrian in-kind contribution to ESO. A command-line tool skycalc_cli is available to execute the SkyCalc backend engine through an API. The C source code is available to download.

Demo
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pyLIMA: Microlensing Modeling and Simulation

Bachelet, E. et al.

The python Lightcurve Identification and Microlensing Analysis (pyLIMA) is the first microlensing software fully open-source. Motivated by the data deluge that the Roman telescope microlensing survey will collect, it is designed to allow easy and efficient modeling of microlensing data (including parallalization), but is also capable of simulations. It is mainly written in python and object oriented. The codes contains lot of examples, a complete documentation and is continuously developed.

Code Language(s): Python3

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pyLIMA: Microlensing Modeling and Simulation

Bachelet, E. et al.

The python Lightcurve Identification and Microlensing Analysis (pyLIMA) is the first microlensing software fully open-source. Motivated by the data deluge that the Roman telescope microlensing survey will collect, it is designed to allow easy and efficient modeling of microlensing data (including parallalization), but is also capable of simulations. It is mainly written in python and object oriented. The codes contains lot of examples, a complete documentation and is continuously developed.

About Demo
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RVLIN: A Fast Maximum Likelihood Method for Fitting Multiplanet Keplerian Curves to RV Data Coded in IDL

Jason Wright and Andrew Howard

An IDL routine for fitting multiple Keplerian curves (i.e. those that neglect planet-planet interactions) to radial velocity data. Handles heteroschedastic data, and constraints on P, e, phase, and transit timing. It is fast because it solves, via maximum likelihood, for all parameters other than P, e, and phase with a linear least-squares matrix inversion. It then determines the remaining 3*n parameters (where n is the number of planets) via a Levenberg-Marquardt optimization. The method is described in Wright & Howard ApJS 182, 205. Uncertainties in the fitted parameters can be determined via bootstrapping using BOOTTRAN (Wang et al. ApJ 761, 46).

Code Language(s): IDL

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https://emac.gsfc.nasa.gov?related_resource=50f4454d-7b16-44f0-ab53-f47810ac0a04

RVLIN: A Fast Maximum Likelihood Method for Fitting Multiplanet Keplerian Curves to RV Data Coded in IDL

Jason Wright and Andrew Howard

An IDL routine for fitting multiple Keplerian curves (i.e. those that neglect planet-planet interactions) to radial velocity data. Handles heteroschedastic data, and constraints on P, e, phase, and transit timing. It is fast because it solves, via maximum likelihood, for all parameters other than P, e, and phase with a linear least-squares matrix inversion. It then determines the remaining 3*n parameters (where n is the number of planets) via a Levenberg-Marquardt optimization. The method is described in Wright & Howard ApJS 182, 205. Uncertainties in the fitted parameters can be determined via bootstrapping using BOOTTRAN (Wang et al. ApJ 761, 46).

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ExoplAn3T: Exoplanet Analysis and 3D visualization Tool

Zinzi, A.; Turrini, D.; Alei E.; Verrecchia F.

ExoplAn3T is the SSDC scientific webtool providing access to several on-line exoplanet catalogues (i.e., NASA Exoplanetary Archive, The Extrasolar Planet Encyclopedia and ExoMerCat) and is designed and optimized for the study of exoplanetary systems as a whole, rather than to extract information on single planets. The tool applies a two step procedure: a "planetary query", aimed at finding exoplanets having the characteristics required by the user and a series of "system queries" (one query for every exosystem found in the first step) looking for all the planets belonging to each exosystem in which the exoplanets of the "planetary query" are found.

Code Language(s): N/A

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https://emac.gsfc.nasa.gov?related_resource=02c2ff7c-e509-4914-8ca5-17be6c8c0c31

ExoplAn3T: Exoplanet Analysis and 3D visualization Tool

Zinzi, A.; Turrini, D.; Alei E.; Verrecchia F.

ExoplAn3T is the SSDC scientific webtool providing access to several on-line exoplanet catalogues (i.e., NASA Exoplanetary Archive, The Extrasolar Planet Encyclopedia and ExoMerCat) and is designed and optimized for the study of exoplanetary systems as a whole, rather than to extract information on single planets. The tool applies a two step procedure: a "planetary query", aimed at finding exoplanets having the characteristics required by the user and a series of "system queries" (one query for every exosystem found in the first step) looking for all the planets belonging to each exosystem in which the exoplanets of the "planetary query" are found.

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https://emac.gsfc.nasa.gov?related_resource=7e7481e3-406e-430f-a476-0baac6e69f7e
Gemini Planet Imager Data Reduction Pipeline: Official data reduction pipeline for the GPI instrument integral field spectrograph

M. Perrin, J. Maire and the GPI Data Analysis Team

The Gemini Planet Imager Data Pipeline allows transformation of raw data from GPI into calibrated spectral and polarimetric data cubes. It also provides some basic capabilities for PSF suppression through differential imaging, and for astrometry and spectrophotometry of detected sources. See complete documentation here.

Code Language(s): IDL

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https://emac.gsfc.nasa.gov?related_resource=7e7481e3-406e-430f-a476-0baac6e69f7e

Gemini Planet Imager Data Reduction Pipeline: Official data reduction pipeline for the GPI instrument integral field spectrograph

M. Perrin, J. Maire and the GPI Data Analysis Team

The Gemini Planet Imager Data Pipeline allows transformation of raw data from GPI into calibrated spectral and polarimetric data cubes. It also provides some basic capabilities for PSF suppression through differential imaging, and for astrometry and spectrophotometry of detected sources. See complete documentation here.

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Habitable-exoplanets-visualisation: Visualization of the NASA Exoplanets Archive open data of exoplanets that are similar to the Earth.

Ermishin Andrey

Visualization of the open data from the NASA Exoplanets Archive of planets outside the solar system that are similar to the Earth and habitable. By means of NASA API exoplanets were parsed and stored to SQLite database: "content.sqlite". There are two visualizations of the parsed data: - Python 3 file "khistogram.py" samples data into "khistogram.js" which is used by "khistogram.htm" to visualize data with D3.js library; - Python 3 file"kbchart.py" samples data into "kbchart.js" which is used by "kbchart.htm" to visualize data with Google BubbleChart.

Code Language(s): HTML, JavaScript, Python3

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https://emac.gsfc.nasa.gov?related_resource=1f2538dd-5c28-49d2-96ff-8ae4839dac12

Habitable-exoplanets-visualisation: Visualization of the NASA Exoplanets Archive open data of exoplanets that are similar to the Earth.

Ermishin Andrey

Visualization of the open data from the NASA Exoplanets Archive of planets outside the solar system that are similar to the Earth and habitable. By means of NASA API exoplanets were parsed and stored to SQLite database: "content.sqlite". There are two visualizations of the parsed data: - Python 3 file "khistogram.py" samples data into "khistogram.js" which is used by "khistogram.htm" to visualize data with D3.js library; - Python 3 file"kbchart.py" samples data into "kbchart.js" which is used by "kbchart.htm" to visualize data with Google BubbleChart.

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HARDCORE Web Interface

Yosef Miller

The HARDCORE Web Interface tool is a user-friendly web-based application of HARDCORE. This tool allows users to easily calculate minimum, maximum, and marginal core radius fractions (CRFmin, CRFmax, CRFmarg) for a solid exoplanet based on mass and radius limits.

Code Language(s): N/A

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https://emac.gsfc.nasa.gov?related_resource=88f2a333-d081-4bcd-908d-14c4d8cf702f

HARDCORE Web Interface

Yosef Miller

The HARDCORE Web Interface tool is a user-friendly web-based application of HARDCORE. This tool allows users to easily calculate minimum, maximum, and marginal core radius fractions (CRFmin, CRFmax, CRFmarg) for a solid exoplanet based on mass and radius limits.

Demo
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https://emac.gsfc.nasa.gov?related_resource=b58496a5-05ee-47d2-af17-2f6cbc45b9fc
ARTES: Radiative transfer of polarized light in 3D exoplanet atmospheres

Tomas Stolker

ARTES is a 3D Monte Carlo radiative transfer code for polarized scattered light simulations of exoplanet atmospheres. The code can be used for post-processing of a pre-calculated or parametrized atmosphere structure. Multiple scattering, absorption, and polarization are fully treated and the output includes an image, spectrum, or phase curve of reflected stellar light or thermal photons. Several tools are included for calculating opacities and scattering matrices of molecules and clouds but the user can also adopt their own opacities.

Code Language(s): Fortran, Python3

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https://emac.gsfc.nasa.gov?related_resource=b58496a5-05ee-47d2-af17-2f6cbc45b9fc

ARTES: Radiative transfer of polarized light in 3D exoplanet atmospheres

Tomas Stolker

ARTES is a 3D Monte Carlo radiative transfer code for polarized scattered light simulations of exoplanet atmospheres. The code can be used for post-processing of a pre-calculated or parametrized atmosphere structure. Multiple scattering, absorption, and polarization are fully treated and the output includes an image, spectrum, or phase curve of reflected stellar light or thermal photons. Several tools are included for calculating opacities and scattering matrices of molecules and clouds but the user can also adopt their own opacities.

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TTVFaster: First Order Eccentricity Transit Timing Variations (TTVs)

Agol & Deck (2016)

Analytic model for transit-timing variations which is accurate to first order in eccentricity and mass-ratio.

Code Language(s): C, IDL, Julia, Python3

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https://emac.gsfc.nasa.gov?related_resource=dae31f6b-a353-4ebf-ac2a-39d4ccb89a2d

TTVFaster: First Order Eccentricity Transit Timing Variations (TTVs)

Agol & Deck (2016)

Analytic model for transit-timing variations which is accurate to first order in eccentricity and mass-ratio.

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Exorings: Python Tools for Displaying and Fitting Giant Extrasolar Planet Ring Systems

M. Kenworthy

Exorings is a Python 3 package that simulates giant Hill sphere ring systems, and generates the resultant light curve when a star passes behind the rings. The star and rings are modelled on a Cartesian grid with no optimisation for calculation speed. The module can create plots of the ring system using vector graphics in a PDF suitable for publications. There is an interactive ring fitting code that allows manual addition and removal of ring edges and optical depths, with Amoeba optimization for the ring transmissions. The rings are saved in a FITS table with ring system metadata.

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?related_resource=40ae4b41-5d95-4d54-a052-59fdcc9d026b

Exorings: Python Tools for Displaying and Fitting Giant Extrasolar Planet Ring Systems

M. Kenworthy

Exorings is a Python 3 package that simulates giant Hill sphere ring systems, and generates the resultant light curve when a star passes behind the rings. The star and rings are modelled on a Cartesian grid with no optimisation for calculation speed. The module can create plots of the ring system using vector graphics in a PDF suitable for publications. There is an interactive ring fitting code that allows manual addition and removal of ring edges and optical depths, with Amoeba optimization for the ring transmissions. The rings are saved in a FITS table with ring system metadata.

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EDI-Vetter Unplugged: Transit Signal False Positive Vetting Tool

Zink, J. K.

This program was originally designed to identify false positive transit signals in K2 photometry, but has been modified to play nicely with the Transit Least Squares software package. It contains several thresholds which mimic visual inspection and help automate the transit detection process.

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?related_resource=bd857f5b-cc9a-4ae4-a7ad-b2e506e54740

EDI-Vetter Unplugged: Transit Signal False Positive Vetting Tool

Zink, J. K.

This program was originally designed to identify false positive transit signals in K2 photometry, but has been modified to play nicely with the Transit Least Squares software package. It contains several thresholds which mimic visual inspection and help automate the transit detection process.

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SWEET-Cat: A catalog of stellar parameters for stars with planets

Planet team at the Instituto de Astrofísica e Ciências do Espaço

A catalog of atmospheric parameters for planet-host stars. It compiles sets of atmospheric parameters previously published in literature (including Teff, logg, and [Fe / H]) and, whenever possible, derives them using the same uniform methodology.

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?related_resource=0b8381f1-897c-40db-8aca-06b55541ccb5

SWEET-Cat: A catalog of stellar parameters for stars with planets

Planet team at the Instituto de Astrofísica e Ciências do Espaço

A catalog of atmospheric parameters for planet-host stars. It compiles sets of atmospheric parameters previously published in literature (including Teff, logg, and [Fe / H]) and, whenever possible, derives them using the same uniform methodology.

Demo
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https://emac.gsfc.nasa.gov?related_resource=067ca9ad-34f6-407c-9daf-afd686c6349b
MCMCI: MCMC-based analysis of light curves or RV time series with interpolation within stellar isochrones

Bonfanti A., Gillon M.

The MCMCI tool offers the opportunity to perform an integrated analysis of an exoplanetary system without splitting it into the preliminary stellar characterisation through theoretical models.The MCMCI combines the Markov chain Monte Carlo approach of analysing photometric or radial velocity time series with a proper interpolation within stellar evolutionary isochrones and tracks, to be performed at each chain step, to retrieve stellar theoretical parameters such as age, mass, and radius. This approach favours a close interaction between lightcurve analysis and isochrones, so that the parameters recovered at each step of the MCMC enter as inputs for purposes of the isochrone placement.

Code Language(s): Fortran

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MCMCI: MCMC-based analysis of light curves or RV time series with interpolation within stellar isochrones

Bonfanti A., Gillon M.

The MCMCI tool offers the opportunity to perform an integrated analysis of an exoplanetary system without splitting it into the preliminary stellar characterisation through theoretical models.The MCMCI combines the Markov chain Monte Carlo approach of analysing photometric or radial velocity time series with a proper interpolation within stellar evolutionary isochrones and tracks, to be performed at each chain step, to retrieve stellar theoretical parameters such as age, mass, and radius. This approach favours a close interaction between lightcurve analysis and isochrones, so that the parameters recovered at each step of the MCMC enter as inputs for purposes of the isochrone placement.

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PlanetSlicer: Phase Curve Fitting for Emission and Reflection via the Orange Slice Model

Thorngren, Daniel P.; Mayorga, Laura C.

This is a small package for fitting brightness maps to phase curves using the "orange-slice" method. For self-luminous objects, this follows Cowan and Agol (2008) "Inverting Phase Functions to Map Exoplanets". We have extended this approach to diffuse reflected light assuming Lambertian reflectance, in Mayorga et. al (2020). In both cases, the model supposes that a spherical object can be divided into slices of constant brightness (or albedo) which may be integrated to yield the total flux observed, given the angles of observation.

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?related_resource=e5536d0e-c784-4d4b-8999-da5add804a5a

PlanetSlicer: Phase Curve Fitting for Emission and Reflection via the Orange Slice Model

Thorngren, Daniel P.; Mayorga, Laura C.

This is a small package for fitting brightness maps to phase curves using the "orange-slice" method. For self-luminous objects, this follows Cowan and Agol (2008) "Inverting Phase Functions to Map Exoplanets". We have extended this approach to diffuse reflected light assuming Lambertian reflectance, in Mayorga et. al (2020). In both cases, the model supposes that a spherical object can be divided into slices of constant brightness (or albedo) which may be integrated to yield the total flux observed, given the angles of observation.

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https://emac.gsfc.nasa.gov?related_resource=9e864265-ebde-4354-9c02-025f64c28150
kima: Exoplanet detection in RVs with DNest4 and GPs

Faria et al. 2018, JOSS, 3, 487

kima is a package for the detection and characterization of exoplanets using RV data. It fits a sum of Keplerian curves to a time series of RV measurements and calculates the evidence for models with a fixed number Np of Keplerian signals, or after marginalising over Np. A Gaussian process with a quasi-periodic kernel can be used as a noise model, to deal with activity-induced signals. The hyperparameters of the GP are inferred together with the orbital parameters. The code is written in C++ and includes a helper Python package, pykima, which facilitates the analysis of the results.

Code Language(s): C, C++, Fortran, Python3

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https://emac.gsfc.nasa.gov?related_resource=9e864265-ebde-4354-9c02-025f64c28150

kima: Exoplanet detection in RVs with DNest4 and GPs

Faria et al. 2018, JOSS, 3, 487

kima is a package for the detection and characterization of exoplanets using RV data. It fits a sum of Keplerian curves to a time series of RV measurements and calculates the evidence for models with a fixed number Np of Keplerian signals, or after marginalising over Np. A Gaussian process with a quasi-periodic kernel can be used as a noise model, to deal with activity-induced signals. The hyperparameters of the GP are inferred together with the orbital parameters. The code is written in C++ and includes a helper Python package, pykima, which facilitates the analysis of the results.

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ShellSpec: Lightcurves, spectra and images of binaries and exoplanets with moving circum-object material

Jan Budaj

Program SHELLSPEC is designed to calculate lightcurves, spectra and images of interacting binaries and extrasolar planets immersed in a moving circumstellar matter (CM). It solves a simple radiative transfer along the line of sight in 3D moving CM. Roche model with a reflection effect and synthetic spectra from the stellar atmosphere models can be used as a boundary condition for the radiative transfer. Dust, including non-isotropic Mie scattering, can also be taken into account. The assumptions include LTE and optional known state quantities and velocity fields in 3D.

Code Language(s): Fortran

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https://emac.gsfc.nasa.gov?related_resource=417d1844-72d9-4614-b200-9d6f606c0b77

ShellSpec: Lightcurves, spectra and images of binaries and exoplanets with moving circum-object material

Jan Budaj

Program SHELLSPEC is designed to calculate lightcurves, spectra and images of interacting binaries and extrasolar planets immersed in a moving circumstellar matter (CM). It solves a simple radiative transfer along the line of sight in 3D moving CM. Roche model with a reflection effect and synthetic spectra from the stellar atmosphere models can be used as a boundary condition for the radiative transfer. Dust, including non-isotropic Mie scattering, can also be taken into account. The assumptions include LTE and optional known state quantities and velocity fields in 3D.

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EXOCROSS: A general program for generating spectra from molecular line lists

ExoCross is a Fortran code for generating spectra (emission, absorption) and thermodynamic properties (partition function, specific heat etc.) from molecular line lists including ExoMol and HITRAN. Input is taken in several formats, including ExoMol and HITRAN formats. ExoCross can work with several line profiles such as Doppler, Lorentzian and Voigt and support several broadening schemes. ExoCross supports calculations of lifetimes, cooling functions, specific heats and other properties. It is capable of simulating non-LTE spectra using a two-temperature approach as well as custom-built models.

Code Language(s): Fortran

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https://emac.gsfc.nasa.gov?related_resource=45fa4b5c-c189-437a-b5ef-b312e982a32e

EXOCROSS: A general program for generating spectra from molecular line lists

ExoCross is a Fortran code for generating spectra (emission, absorption) and thermodynamic properties (partition function, specific heat etc.) from molecular line lists including ExoMol and HITRAN. Input is taken in several formats, including ExoMol and HITRAN formats. ExoCross can work with several line profiles such as Doppler, Lorentzian and Voigt and support several broadening schemes. ExoCross supports calculations of lifetimes, cooling functions, specific heats and other properties. It is capable of simulating non-LTE spectra using a two-temperature approach as well as custom-built models.

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Spotrod: A Semi-analytic Model for Transits of Spotted Stars

Bence Béky; David M. Kipping; Matthew J. Holman

A library for numerical simulation of transit lightcurves for a planet transiting a star with homogeneous, circular spots. Homogeneous umbra and homogeneous penumbra can be modelled using two concentric spots. Written in C, with Python bindings.

Code Language(s): C, Python3

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https://emac.gsfc.nasa.gov?related_resource=b89cf8e5-d7a4-4669-af63-5156852d3b29

Spotrod: A Semi-analytic Model for Transits of Spotted Stars

Bence Béky; David M. Kipping; Matthew J. Holman

A library for numerical simulation of transit lightcurves for a planet transiting a star with homogeneous, circular spots. Homogeneous umbra and homogeneous penumbra can be modelled using two concentric spots. Written in C, with Python bindings.

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MAYONNAISE processing pipeline: A morphological components analysis pipeline for circumstellar disks and exoplanets imaging

Pairet, B. et al.

MAYO is a pipeline for exoplanet and disk high-contrast imaging from ADI datasets. For more information, please refer to Pairet, Benoît, Faustine Cantalloube, and Laurent Jacques. "MAYONNAISE: a morphological components analysis pipeline for circumstellar disks and exoplanets imaging in the near infrared.", Monthly Notices of the Royal Astronomical Society, 2021.

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?related_resource=2f8ed766-5614-4e70-a0e6-ff39fb2da8ea

MAYONNAISE processing pipeline: A morphological components analysis pipeline for circumstellar disks and exoplanets imaging

Pairet, B. et al.

MAYO is a pipeline for exoplanet and disk high-contrast imaging from ADI datasets. For more information, please refer to Pairet, Benoît, Faustine Cantalloube, and Laurent Jacques. "MAYONNAISE: a morphological components analysis pipeline for circumstellar disks and exoplanets imaging in the near infrared.", Monthly Notices of the Royal Astronomical Society, 2021.

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VARTOOLS: Command-line program for analyzing astronomical time-series data

Hartman, J.D. and Bakos, G.A.

The VARTOOLS program is a command line utility that provides tools for processing and analyzing astronomical time series data, especially light curves. It includes methods for calculating variability/periodicity statistics of light curves; for filtering, transforming, and otherwise modifying light curves; and for modeling light curves. It is intended primarily for batch processing a large number of light curves. The program is run by issuing a sequence of commands to perform actions on light curves, each command is executed in turn with the resulting light curves passed to the next command. Statistics computed by each command are sent to stdout as an ascii table.

Code Language(s): C, Shell

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VARTOOLS: Command-line program for analyzing astronomical time-series data

Hartman, J.D. and Bakos, G.A.

The VARTOOLS program is a command line utility that provides tools for processing and analyzing astronomical time series data, especially light curves. It includes methods for calculating variability/periodicity statistics of light curves; for filtering, transforming, and otherwise modifying light curves; and for modeling light curves. It is intended primarily for batch processing a large number of light curves. The program is run by issuing a sequence of commands to perform actions on light curves, each command is executed in turn with the resulting light curves passed to the next command. Statistics computed by each command are sent to stdout as an ascii table.

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Kepler DR25 Robovetter: Automatic vetting of Kepler DR25 TCEs into Planet Candidates and False Positives

Thompson et al. 2018, ApJS, 235, 38

The DR25 Kepler Robovetter is a robotic decision-making code that dispositions each Threshold Crossing Event (TCE) from the Kepler pipeline into Planet Candidates (PCs) and False Positives (FPs). The Robovetter also provides four major flags to designate each FP TCE as Not Transit-Like (NTL), a Stellar Eclipse (SS), a Centroid Offset (CO), and/or an Ephemeris Match (EM). It also produces a score ranging from 0.0 to 1.0 that indicates the Robovetter's disposition confidence, where 1.0 indicates strong confidence in PC, and 0.0 indicates strong confidence in FP. Finally, the Robovetter provides comments in a text string that indicate the specific tests each FP TCE fails.

Code Language(s): C++

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Kepler DR25 Robovetter: Automatic vetting of Kepler DR25 TCEs into Planet Candidates and False Positives

Thompson et al. 2018, ApJS, 235, 38

The DR25 Kepler Robovetter is a robotic decision-making code that dispositions each Threshold Crossing Event (TCE) from the Kepler pipeline into Planet Candidates (PCs) and False Positives (FPs). The Robovetter also provides four major flags to designate each FP TCE as Not Transit-Like (NTL), a Stellar Eclipse (SS), a Centroid Offset (CO), and/or an Ephemeris Match (EM). It also produces a score ranging from 0.0 to 1.0 that indicates the Robovetter's disposition confidence, where 1.0 indicates strong confidence in PC, and 0.0 indicates strong confidence in FP. Finally, the Robovetter provides comments in a text string that indicate the specific tests each FP TCE fails.

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ChromaStarPy: Transit light-curve modelling integrated with stellar atmosphere and surface intensity modeling

Short, C. Ian and Bennett, Philip D.

An approximate stellar atmosphere and spectrum modelling code in Python that includes in situ modelling of the transit light-curve due to a "small" exo-planet occulting the computed stellar surface intensity distribution.

Code Language(s): Python3

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ChromaStarPy: Transit light-curve modelling integrated with stellar atmosphere and surface intensity modeling

Short, C. Ian and Bennett, Philip D.

An approximate stellar atmosphere and spectrum modelling code in Python that includes in situ modelling of the transit light-curve due to a "small" exo-planet occulting the computed stellar surface intensity distribution.

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SPInS: Stellar Parameters INferred Systematically

Reese, D. R. and Lebreton, Y.

SPInS is a Python tool that takes in a set of photometric, spectroscopic, interferometric, and/or global asteroseismic observational constraints, and uses a Bayesian approach to find the probability distribution functions of stellar parameters, such as the age, mass, and radius of a star, as well as error bars and correlations between these parameters. At the heart of the code is a Markov chain Monte Carlo solver coupled with interpolation within a pre-computed grid of stellar models. Priors can be considered, such as the initial mass function or the stellar formation rate. SPInS can characterize single stars or coeval stars, such as members of binary systems or of stellar clusters.

Code Language(s): Fortran, Python3

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SPInS: Stellar Parameters INferred Systematically

Reese, D. R. and Lebreton, Y.

SPInS is a Python tool that takes in a set of photometric, spectroscopic, interferometric, and/or global asteroseismic observational constraints, and uses a Bayesian approach to find the probability distribution functions of stellar parameters, such as the age, mass, and radius of a star, as well as error bars and correlations between these parameters. At the heart of the code is a Markov chain Monte Carlo solver coupled with interpolation within a pre-computed grid of stellar models. Priors can be considered, such as the initial mass function or the stellar formation rate. SPInS can characterize single stars or coeval stars, such as members of binary systems or of stellar clusters.

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VLT-sphere: Automatic VLT/SPHERE data reduction and analysis

Arthur Vigan

The high-contrast imager SPHERE at the Very Large Telescope combines extreme adaptive optics and coronagraphy to directly image exoplanets in the near-infrared. The vlt-sphere package enables easy reduction of the data coming from IRDIS and IFS, the two near-infrared subsystems of SPHERE. The package relies on the official ESO pipeline (ascl:1402.010), which must be installed separately.

Code Language(s): Python3

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VLT-sphere: Automatic VLT/SPHERE data reduction and analysis

Arthur Vigan

The high-contrast imager SPHERE at the Very Large Telescope combines extreme adaptive optics and coronagraphy to directly image exoplanets in the near-infrared. The vlt-sphere package enables easy reduction of the data coming from IRDIS and IFS, the two near-infrared subsystems of SPHERE. The package relies on the official ESO pipeline (ascl:1402.010), which must be installed separately.

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ATMO: 1D-2D radiative/convective atmospheric code

Tremblin P. et al. (see description)

ATMO is a 1D-2D atmospheric code for the study of the atmosphere of brown dwarfs and exoplanets. The code has originally been developed at the University of Exeter (here) and is currently a collaboration between different groups across the globe. The main developers are: 1D and 2D newton solver: P. Tremblin Radiative transfer: D. Amundsen, P. Tremblin Opacities: D. Amundsen, M. Phillips, R. Ridgway, J. Goyal Equilibrium chemistry: P. Tremblin, B. Drummond, J. Goyal Condensation and rainouts: P. Tremblin, J. Goyal Out-of-equilibrium chemistry: O. Venot, E. Hebrard, B. Drummond Convection: P. Tremblin, M. Phillips Retrieval: D. Sing

Code Language(s): N/A

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ATMO: 1D-2D radiative/convective atmospheric code

Tremblin P. et al. (see description)

ATMO is a 1D-2D atmospheric code for the study of the atmosphere of brown dwarfs and exoplanets. The code has originally been developed at the University of Exeter (here) and is currently a collaboration between different groups across the globe. The main developers are: 1D and 2D newton solver: P. Tremblin Radiative transfer: D. Amundsen, P. Tremblin Opacities: D. Amundsen, M. Phillips, R. Ridgway, J. Goyal Equilibrium chemistry: P. Tremblin, B. Drummond, J. Goyal Condensation and rainouts: P. Tremblin, J. Goyal Out-of-equilibrium chemistry: O. Venot, E. Hebrard, B. Drummond Convection: P. Tremblin, M. Phillips Retrieval: D. Sing

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The Joker: A custom Monte Carlo sampler for the two-body problem

A. Price-Whelan, D. W. Hogg, D. Foreman-Mackey

The Joker is a custom Monte Carlo sampler for the two-body problem that generates posterior samplings in Keplerian orbital parameters given radial velocity observations of stars. It is designed to deliver converged posterior samplings even when the radial velocity measurements are sparse or very noisy. It is therefore useful for constraining the orbital properties of binary star or star-planet systems. Though it fundamentally assumes that any system has two massive bodies (and only the primary is observed), The Joker can also be used for hierarchical systems in which the velocity perturbations from a third or other bodies are much longer than the dominant companion.

Code Language(s): Cython, Python3

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The Joker: A custom Monte Carlo sampler for the two-body problem

A. Price-Whelan, D. W. Hogg, D. Foreman-Mackey

The Joker is a custom Monte Carlo sampler for the two-body problem that generates posterior samplings in Keplerian orbital parameters given radial velocity observations of stars. It is designed to deliver converged posterior samplings even when the radial velocity measurements are sparse or very noisy. It is therefore useful for constraining the orbital properties of binary star or star-planet systems. Though it fundamentally assumes that any system has two massive bodies (and only the primary is observed), The Joker can also be used for hierarchical systems in which the velocity perturbations from a third or other bodies are much longer than the dominant companion.

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EvapMass: Minimum mass of planets predictor

Owen, James E. & Campos Estrada, Beatriz

EvapMass predicts the minimum masses of planets in multi-planet systems using the photoevaporation-driven evolution model. The planetary system requires both a planet above and below the radius gap to be useful for this test. EvapMass includes an example Jupyter notebook for the Kepler-36 system. EvalMass can be used to identify TESS systems that warrant radial-velocity follow-up to further test the photoevaporation model.

Code Language(s): Python3

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EvapMass: Minimum mass of planets predictor

Owen, James E. & Campos Estrada, Beatriz

EvapMass predicts the minimum masses of planets in multi-planet systems using the photoevaporation-driven evolution model. The planetary system requires both a planet above and below the radius gap to be useful for this test. EvapMass includes an example Jupyter notebook for the Kepler-36 system. EvalMass can be used to identify TESS systems that warrant radial-velocity follow-up to further test the photoevaporation model.

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Lightkurve Web Interface: Easy to use Web Interface of the Lightkurve Python Package

Yosef Miller

The Lightkurve Web Interface tool is a user-friendly web-based application of the Lightkurve python package. This tool allows users to quickly produce light curves based on time series data obtained by NASA's Kepler, K2, and TESS missions.

Code Language(s): N/A

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Lightkurve Web Interface: Easy to use Web Interface of the Lightkurve Python Package

Yosef Miller

The Lightkurve Web Interface tool is a user-friendly web-based application of the Lightkurve python package. This tool allows users to quickly produce light curves based on time series data obtained by NASA's Kepler, K2, and TESS missions.

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Stella: Convolutional Neural Networks for Flare Identification in TESS 2-minute Data

Feinstein et al. 2020a,b

The purpose of stella is to identify flares in TESS short-cadence data with a convolutional neural network (CNN). In its simplest form, stella takes a pre-trained CNN (available on MAST: https://archive.stsci.edu/hlsp/stella) and a light curve (time, flux, and flux error) and returns a probability light curve. The cadences in the probability light curve are values between 0 and 1, where 1 means the CNN believes there is a flare there. It takes < 1 minute to predict flares on a single light curve. Users also have the ability to train their own customized CNN architecture. The stella software also includes modules to measure rotation periods and fit flares using simple exponential models.

Code Language(s): Python3

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Stella: Convolutional Neural Networks for Flare Identification in TESS 2-minute Data

Feinstein et al. 2020a,b

The purpose of stella is to identify flares in TESS short-cadence data with a convolutional neural network (CNN). In its simplest form, stella takes a pre-trained CNN (available on MAST: https://archive.stsci.edu/hlsp/stella) and a light curve (time, flux, and flux error) and returns a probability light curve. The cadences in the probability light curve are values between 0 and 1, where 1 means the CNN believes there is a flare there. It takes < 1 minute to predict flares on a single light curve. Users also have the ability to train their own customized CNN architecture. The stella software also includes modules to measure rotation periods and fit flares using simple exponential models.

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RV Jitter prediction code: Predicting RV jitter due to stellar oscillations and granulation using stellar parameters

Yu et al. 2018

Radial-velocity jitter due to intrinsic stellar variability introduces challenges when characterizing exoplanet systems, particularly when studying small (sub-Neptune-sized) planets orbiting solar-type stars. This code will be valuable for anticipating the radial-velocity stellar noise level of exoplanet host stars, and hence be useful for their follow-up spectroscopic observations. Our prediction can be also used to set a prior for the jitter term as a component when modeling the Keplerian orbits of the exoplanets.

Code Language(s): Python3

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RV Jitter prediction code: Predicting RV jitter due to stellar oscillations and granulation using stellar parameters

Yu et al. 2018

Radial-velocity jitter due to intrinsic stellar variability introduces challenges when characterizing exoplanet systems, particularly when studying small (sub-Neptune-sized) planets orbiting solar-type stars. This code will be valuable for anticipating the radial-velocity stellar noise level of exoplanet host stars, and hence be useful for their follow-up spectroscopic observations. Our prediction can be also used to set a prior for the jitter term as a component when modeling the Keplerian orbits of the exoplanets.

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TRICERATOPS: Bayesian Vetting and Validation Tool for Transiting Exoplanet Candidates

Giacalone, S. et al.

TRICERATOPS is a Bayesian vetting and validation tool for TESS planet candidates. For a given planet candidate, the tool calculates the probabilities of several astrophysical transit-producing scenarios using the TESS light curve, information about nearby stars, and follow-up observations (e.g., high-resolution imaging, spectroscopy, and time-series photometry). Using these probabilities, TRICERATOPS calculates a false positive probability (the overall probability of the transit being caused by an astrophysical false positive) and a nearby false positive probability (the probability of the transit being caused by an off-target event around a nearby star).

Code Language(s): Python3

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TRICERATOPS: Bayesian Vetting and Validation Tool for Transiting Exoplanet Candidates

Giacalone, S. et al.

TRICERATOPS is a Bayesian vetting and validation tool for TESS planet candidates. For a given planet candidate, the tool calculates the probabilities of several astrophysical transit-producing scenarios using the TESS light curve, information about nearby stars, and follow-up observations (e.g., high-resolution imaging, spectroscopy, and time-series photometry). Using these probabilities, TRICERATOPS calculates a false positive probability (the overall probability of the transit being caused by an astrophysical false positive) and a nearby false positive probability (the probability of the transit being caused by an off-target event around a nearby star).

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xwavecal: A Blind Wavelength Calibration Algorithm for Echelle Spectrographs

Brandt, G. M. et al.

A library of routines for wavelength calibrating echelle spectrographs for high precision radial velocity work. Calibrates the instrument without any input for known emission line wavelengths and positions. A limited data-reduction pipeline is included.

Code Language(s): Python3

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xwavecal: A Blind Wavelength Calibration Algorithm for Echelle Spectrographs

Brandt, G. M. et al.

A library of routines for wavelength calibrating echelle spectrographs for high precision radial velocity work. Calibrates the instrument without any input for known emission line wavelengths and positions. A limited data-reduction pipeline is included.

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Wotan: Remove Trends from Time-series Data

Michael Hippke

Wotan offers free and open source algorithms to automagically remove trends from time-series data. Python open source. Available detrending algorithms include: Time-windowed sliders with location estimates, splines, polynomials and sines, regressions, fitting a model that is a sum of Gaussian bases, Gaussian Processes. Available features: Filter lengths, break tolerances, edge cutoffs, tuning parameters, transit masks.

Code Language(s): Python3

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Wotan: Remove Trends from Time-series Data

Michael Hippke

Wotan offers free and open source algorithms to automagically remove trends from time-series data. Python open source. Available detrending algorithms include: Time-windowed sliders with location estimates, splines, polynomials and sines, regressions, fitting a model that is a sum of Gaussian bases, Gaussian Processes. Available features: Filter lengths, break tolerances, edge cutoffs, tuning parameters, transit masks.

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Juliet: A Versatile Modeling tool for Transiting and Non-transiting Exoplanetary Systems

Espinoza et al.

Juliet is a versatile modelling tool for transiting and non-transiting exoplanetary systems that allows to perform quick-and-easy fits to data coming from transit photometry, radial velocity or both using bayesian inference and, in particular, using Nested Sampling in order to allow both efficient fitting and proper model comparison. This pip-installable python library (pip install juliet) also allows to model these time-series using either simple linear models and/or more involved correlated noise on both photometry and radial-velocities through Gaussian Processes. Full documentation is available here.

Code Language(s): Python3

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Juliet: A Versatile Modeling tool for Transiting and Non-transiting Exoplanetary Systems

Espinoza et al.

Juliet is a versatile modelling tool for transiting and non-transiting exoplanetary systems that allows to perform quick-and-easy fits to data coming from transit photometry, radial velocity or both using bayesian inference and, in particular, using Nested Sampling in order to allow both efficient fitting and proper model comparison. This pip-installable python library (pip install juliet) also allows to model these time-series using either simple linear models and/or more involved correlated noise on both photometry and radial-velocities through Gaussian Processes. Full documentation is available here.

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THOR: Flexible Global Circulation Model to Explore Planetary Atmospheres

Mendonça, J. et al. 2016; Deitrick, R., et al. 2020

THOR is a GCM that solves the three-dimensional non-hydrostatic Euler equations on an icosahedral grid. THOR was designed to run on Graphics Processing Units (GPUs).

Code Language(s): C, C++, Objective C, Python3

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THOR: Flexible Global Circulation Model to Explore Planetary Atmospheres

Mendonça, J. et al. 2016; Deitrick, R., et al. 2020

THOR is a GCM that solves the three-dimensional non-hydrostatic Euler equations on an icosahedral grid. THOR was designed to run on Graphics Processing Units (GPUs).

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pyaneti: A multi-planet Radial Velocity and Transit fit software

Barragán O., Gandolfi D. & Antoniciello G.

pyaneti is a multi-planet radial velocity and transit fit software. The code uses Markov chain Monte Carlo (MCMC) methods with a Bayesian approach and a parallelized ensemble sampler algorithm in Fortran which makes the code fast. It creates posteriors, correlations, and ready-to-publish plots automatically, and handles circular and eccentric orbits. It is capable of multi-planet fitting and handles stellar limb darkening, systemic velocities for multiple instruments, and short and long cadence data, and offers additional capabilities.

Code Language(s): Fortran, Python3

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pyaneti: A multi-planet Radial Velocity and Transit fit software

Barragán O., Gandolfi D. & Antoniciello G.

pyaneti is a multi-planet radial velocity and transit fit software. The code uses Markov chain Monte Carlo (MCMC) methods with a Bayesian approach and a parallelized ensemble sampler algorithm in Fortran which makes the code fast. It creates posteriors, correlations, and ready-to-publish plots automatically, and handles circular and eccentric orbits. It is capable of multi-planet fitting and handles stellar limb darkening, systemic velocities for multiple instruments, and short and long cadence data, and offers additional capabilities.

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Aeolus: Python Library for Object-Oriented Analysis of Atmospheric Model Output

Sergeev, D. E.

Aeolus is a library for analysis and plotting of a climate model output, primarily of the UK Met Office Unified Model when it is used to simulate various planetary atmospheres. Aeolus is built on top of iris and has various functions tailored to exoplanet research, e.g. in the context of tidally-locked exoplanets.

Code Language(s): Python3

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Aeolus: Python Library for Object-Oriented Analysis of Atmospheric Model Output

Sergeev, D. E.

Aeolus is a library for analysis and plotting of a climate model output, primarily of the UK Met Office Unified Model when it is used to simulate various planetary atmospheres. Aeolus is built on top of iris and has various functions tailored to exoplanet research, e.g. in the context of tidally-locked exoplanets.

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POET: Planetary Orbital Evolution due to Tides - Calculate secular orbital evolution

Kaloyan Penev, Luke Bouma, and Joshua Schussler

Calculate secular orbital evolution for star-planet and star-star systems under the combined influence of:

  • Tides in one or both of the objects
  • Age dependent stellar structure
  • Stars losing angular momentum to wind
  • The internal redistribution of angular momentum between the surface and the interior of stars

Code Language(s): C++, Python3

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POET: Planetary Orbital Evolution due to Tides - Calculate secular orbital evolution

Kaloyan Penev, Luke Bouma, and Joshua Schussler

Calculate secular orbital evolution for star-planet and star-star systems under the combined influence of:

  • Tides in one or both of the objects
  • Age dependent stellar structure
  • Stars losing angular momentum to wind
  • The internal redistribution of angular momentum between the surface and the interior of stars

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ExoplanetsSysSim.jl: The SysSim Planet Population Simulator

Eric B. Ford, Matthias He, Danley Hsu, Darin Ragozzine

The ExoplanetsSysSim.jl package generates populations of planetary systems, simulates observations of those systems with a transit survey, and facilitates comparisons of simulated and observed catalogs of planetary systems. Critically, ExoplanetsSysSim accounts for intrinsic correlations in the sizes and orbital periods of planets within a planetary system.

Code Language(s): Julia

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ExoplanetsSysSim.jl: The SysSim Planet Population Simulator

Eric B. Ford, Matthias He, Danley Hsu, Darin Ragozzine

The ExoplanetsSysSim.jl package generates populations of planetary systems, simulates observations of those systems with a transit survey, and facilitates comparisons of simulated and observed catalogs of planetary systems. Critically, ExoplanetsSysSim accounts for intrinsic correlations in the sizes and orbital periods of planets within a planetary system.

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AAS - Timeseries: Read in and Manipulate Time Series in Astropy

Thomas Robitaille, American Astronomical Society (AAS)

The aas-timeseries package has been developed as part of a project between AAS Publishing and the Astropy Project. The goal of this project is to provide astronomers with all the tools needed to make it possible for astronomers to use Astropy to read in and manipulate time series data sets, such as exoplanet transit light curves, produce interactive figures, and easily embed these in a paper. The package is general enough to be usable in other contexts, for example to embed interactive time series figures in personal web pages, or for use in Jupyter notebooks and Jupyter Lab.

Code Language(s): Python3

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AAS - Timeseries: Read in and Manipulate Time Series in Astropy

Thomas Robitaille, American Astronomical Society (AAS)

The aas-timeseries package has been developed as part of a project between AAS Publishing and the Astropy Project. The goal of this project is to provide astronomers with all the tools needed to make it possible for astronomers to use Astropy to read in and manipulate time series data sets, such as exoplanet transit light curves, produce interactive figures, and easily embed these in a paper. The package is general enough to be usable in other contexts, for example to embed interactive time series figures in personal web pages, or for use in Jupyter notebooks and Jupyter Lab.

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ARCHI: An expansion to the CHEOPS mission official pipeline

André M.Silva et al

ARCHI, An expansion to the CHEOPS mission official pipeline, is an additional open-source pipeline to analyse the background stars present in CHEOPS images that are not tracked by the official pipeline. This tool opens a potential for the use of CHEOPS to produce photometric time-series of several close-by targets at once, as well as to use different stars in the image to calibrate systematic errors. Furthermore, there might be cases where the study of the companion light curve can be important for the understanding of contaminations on the main target .

Code Language(s): Python3

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ARCHI: An expansion to the CHEOPS mission official pipeline

André M.Silva et al

ARCHI, An expansion to the CHEOPS mission official pipeline, is an additional open-source pipeline to analyse the background stars present in CHEOPS images that are not tracked by the official pipeline. This tool opens a potential for the use of CHEOPS to produce photometric time-series of several close-by targets at once, as well as to use different stars in the image to calibrate systematic errors. Furthermore, there might be cases where the study of the companion light curve can be important for the understanding of contaminations on the main target .

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VBBinaryLensing: Computation of Microlensing Light Curves and Astrometry

Valerio Bozza, in collaboration with Etienne Bachelet, Fran Bartolic, Ava Hoagg, Thomas Heintz, Markus Hundertmark, Elahe Khalouei

Library for the computation of microlensing light curves and astrometric centroid based on contour integration scheme. The code covers single-lens and binary-lens microlensing, includes finite size of the source, limb darkening, annual and satellite parallax, orbital motion.

Code Language(s): C++, Python3

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VBBinaryLensing: Computation of Microlensing Light Curves and Astrometry

Valerio Bozza, in collaboration with Etienne Bachelet, Fran Bartolic, Ava Hoagg, Thomas Heintz, Markus Hundertmark, Elahe Khalouei

Library for the computation of microlensing light curves and astrometric centroid based on contour integration scheme. The code covers single-lens and binary-lens microlensing, includes finite size of the source, limb darkening, annual and satellite parallax, orbital motion.

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CHIMERA: Exoplanet Emission/Transmission Atmospheric Retrieval Tool

M. Line et al. (J. Lustig-Yaeger, N. Batalha, M. Marley, X. Zhang, A. Wolf)

Flexible atmospheric retrieval tool for exoplanet atmospheres. Can be used for both transmission and emission geometries with options for both the "free" and "chemically consistent" abundance retrievals. Uses correlated-K opacities (R=100) with the random-overlap resort-rebin procedure (Amundsen et al. 2017). Includes full multiple scattering in emission (both planetary and stellar reflected light) using a two stream approximation variant (Toon et al. 1989). Various cloud parameterizations ranging from "grey+power-law" to the "Ackerman & Marley 2001" eddy-sed routine in both emission and transmission. Includes multiple Bayesian samplers, including PyMultiNest (recommended) and Dynesty.

Code Language(s): Python3

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CHIMERA: Exoplanet Emission/Transmission Atmospheric Retrieval Tool

M. Line et al. (J. Lustig-Yaeger, N. Batalha, M. Marley, X. Zhang, A. Wolf)

Flexible atmospheric retrieval tool for exoplanet atmospheres. Can be used for both transmission and emission geometries with options for both the "free" and "chemically consistent" abundance retrievals. Uses correlated-K opacities (R=100) with the random-overlap resort-rebin procedure (Amundsen et al. 2017). Includes full multiple scattering in emission (both planetary and stellar reflected light) using a two stream approximation variant (Toon et al. 1989). Various cloud parameterizations ranging from "grey+power-law" to the "Ackerman & Marley 2001" eddy-sed routine in both emission and transmission. Includes multiple Bayesian samplers, including PyMultiNest (recommended) and Dynesty.

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Forecaster: Empirical, Probabilistic Predictions of Exoplanet Masses & Radii

Jingjing Chen, David Kipping

Forecaster predicts a planet’s mass, based on its radius -OR- predicts a planet’s radius, based on its mass. Forecaster works with both summary statistics and posterior samples. What distinguishes forecaster from other codes is that it is empirical and probabilistic. This means that the algorithm has been trained using the masses and radii of over 300 worlds with precise measurements of their masses and radii. No theoretical relations are used or assumed, the mass-radius relation is simply learned from the data. This also means that the predictions are intrinsically probabilistic (and not deterministic). For a given planetary mass, there isn’t just one possible radius, but a range of radii.

Code Language(s): Python3

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Forecaster: Empirical, Probabilistic Predictions of Exoplanet Masses & Radii

Jingjing Chen, David Kipping

Forecaster predicts a planet’s mass, based on its radius -OR- predicts a planet’s radius, based on its mass. Forecaster works with both summary statistics and posterior samples. What distinguishes forecaster from other codes is that it is empirical and probabilistic. This means that the algorithm has been trained using the masses and radii of over 300 worlds with precise measurements of their masses and radii. No theoretical relations are used or assumed, the mass-radius relation is simply learned from the data. This also means that the predictions are intrinsically probabilistic (and not deterministic). For a given planetary mass, there isn’t just one possible radius, but a range of radii.

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AstroBEAR: An Adaptive Mesh Refinement Code for Computational Astrophysics

Jonathan Carroll-Nellenback, Adam Frank, Baowei Liu, Shule Li, Erica Fogerty, Andrew Cunningham, Sorin Mitran, Zhuo Chen, Kris Yirak, Eddie Hansen, Martin Huarte-Espinosa, Luke Chamandy, Alex Debrecht, Yangyuxin Zou, Atma Anand

AstroBEAR is a parallelized hydrodynamic/MHD simulation code suitable for a variety of astrophysical problems. Derived from the BearCLAW package written by Sorin Mitran, AstroBEAR is designed for 2D and 3D adaptive mesh refinement (AMR), multi-physics simulations. Users write their own project modules by specifying initial conditions and continual processes (such as an inflow condition). In addition, AstroBEAR comes with a number of pre-built physical phenomena such as clumps and winds that can be loaded into a user module.

Code Language(s): Fortran

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AstroBEAR: An Adaptive Mesh Refinement Code for Computational Astrophysics

Jonathan Carroll-Nellenback, Adam Frank, Baowei Liu, Shule Li, Erica Fogerty, Andrew Cunningham, Sorin Mitran, Zhuo Chen, Kris Yirak, Eddie Hansen, Martin Huarte-Espinosa, Luke Chamandy, Alex Debrecht, Yangyuxin Zou, Atma Anand

AstroBEAR is a parallelized hydrodynamic/MHD simulation code suitable for a variety of astrophysical problems. Derived from the BearCLAW package written by Sorin Mitran, AstroBEAR is designed for 2D and 3D adaptive mesh refinement (AMR), multi-physics simulations. Users write their own project modules by specifying initial conditions and continual processes (such as an inflow condition). In addition, AstroBEAR comes with a number of pre-built physical phenomena such as clumps and winds that can be loaded into a user module.

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VIP: Vortex Image Processing package

C. A. Gomez Gonzalez (founder and main contributor), V. Christiaens (current maintainer), R. Farkas, O. Absil, O. Wertz, J. Milli, F. Cantalloube, G. Ruane. More contributors here.

VIP is a python package for angular, reference star and spectral differential imaging, for exoplanet and disk high-contrast imaging. It also contains a number of routines for image preprocessing, performance assessment and companion characterization. The goal of VIP is to integrate open-source, efficient, easy-to-use and well-documented implementations of high-contrast image processing algorithms. VIP started as the effort of Carlos Alberto Gomez Gonzalez, a former PhD student of the VORTEX team (ULiège, Belgium). VIP's development has been led by Dr. Gomez until 2018, and is currently maintained by Dr. Valentin Christiaens (UMonash, Australia). More info here: http://vip.readthedocs.io

Code Language(s): Python3

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VIP: Vortex Image Processing package

C. A. Gomez Gonzalez (founder and main contributor), V. Christiaens (current maintainer), R. Farkas, O. Absil, O. Wertz, J. Milli, F. Cantalloube, G. Ruane. More contributors here.

VIP is a python package for angular, reference star and spectral differential imaging, for exoplanet and disk high-contrast imaging. It also contains a number of routines for image preprocessing, performance assessment and companion characterization. The goal of VIP is to integrate open-source, efficient, easy-to-use and well-documented implementations of high-contrast image processing algorithms. VIP started as the effort of Carlos Alberto Gomez Gonzalez, a former PhD student of the VORTEX team (ULiège, Belgium). VIP's development has been led by Dr. Gomez until 2018, and is currently maintained by Dr. Valentin Christiaens (UMonash, Australia). More info here: http://vip.readthedocs.io

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SVO Filter Profile Service: A repository of Filter information for the Virtual Observatory

Carlos Rodrigo, Spanish Virtual Observatory, CAB, CSIC-INTA.

The SVO Filter Profile Service provides standardized information, including transmission curves and calibration, about more than 6100 astronomical filters. The service is designed to be compliant to the Virtual Observatory Photometry Data Model and all the information is provided both as a web portal and VO services so that other services and applications can access the relevant properties of a filter in a simple way.

Code Language(s): N/A

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SVO Filter Profile Service: A repository of Filter information for the Virtual Observatory

Carlos Rodrigo, Spanish Virtual Observatory, CAB, CSIC-INTA.

The SVO Filter Profile Service provides standardized information, including transmission curves and calibration, about more than 6100 astronomical filters. The service is designed to be compliant to the Virtual Observatory Photometry Data Model and all the information is provided both as a web portal and VO services so that other services and applications can access the relevant properties of a filter in a simple way.

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SVO Theoretical Spectra Server: A Server of Data for over 60 Collections of Theoretical Spectra and Observational Templates

Carlos Rodrigo, Spanish Virtual Observatory, CAB, CSIC-INTA

The SVO Theory Server provides data for more than 60 collections of theoretical spectra and observational templates. Using this web page you can search for spectra in each collection in terms of the corresponding grid parameter ranges, visualize the spectra and/or download them in ascii or VOTable format. You will be able to compare spectra from different collections too. Synthetic Photometry is also available for these spectra and all the filters in the SVO Filter Profile Service.

Code Language(s): N/A

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SVO Theoretical Spectra Server: A Server of Data for over 60 Collections of Theoretical Spectra and Observational Templates

Carlos Rodrigo, Spanish Virtual Observatory, CAB, CSIC-INTA

The SVO Theory Server provides data for more than 60 collections of theoretical spectra and observational templates. Using this web page you can search for spectra in each collection in terms of the corresponding grid parameter ranges, visualize the spectra and/or download them in ascii or VOTable format. You will be able to compare spectra from different collections too. Synthetic Photometry is also available for these spectra and all the filters in the SVO Filter Profile Service.

Demo
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ODUSSEAS: A Machine Learning Tool to Derive Effective Temperature and Metallicity for M Dwarf Stars

A. Antoniadis-Karnavas, S. G. Sousa, E. Delgado-Mena, N. C. Santos, G. D. C. Teixeira, V. Neves

ODUSSEAS is an automatic computational tool able to quickly and reliably derive the Teff and [Fe/H] of M dwarfs using optical spectra obtained by different spectrographs with different resolutions. It is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package “scikit-learn” for predicting the stellar parameters. It is able to derive parameters accurately and with high precision, having precision errors of ~30 K for Teff and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions of between 48000 and 115000 and a signal-to-noise ratio above 20.

Code Language(s): Python3

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ODUSSEAS: A Machine Learning Tool to Derive Effective Temperature and Metallicity for M Dwarf Stars

A. Antoniadis-Karnavas, S. G. Sousa, E. Delgado-Mena, N. C. Santos, G. D. C. Teixeira, V. Neves

ODUSSEAS is an automatic computational tool able to quickly and reliably derive the Teff and [Fe/H] of M dwarfs using optical spectra obtained by different spectrographs with different resolutions. It is based on the measurement of the pseudo equivalent widths for more than 4000 stellar absorption lines and on the use of the machine learning Python package “scikit-learn” for predicting the stellar parameters. It is able to derive parameters accurately and with high precision, having precision errors of ~30 K for Teff and ~0.04 dex for [Fe/H]. The results are consistent for spectra with resolutions of between 48000 and 115000 and a signal-to-noise ratio above 20.

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tpfplotter: TESS Target Pixel File Creator

J. Lillo-Box (Aller et al., 2020, A&A, 635, 128)

tpfplotter is a user-friendly tool to create the TESS Target Pixel Files of your favorite source overplotting the aperture mask used by the SPOC pipeline and the Gaia catalogue to check for possible contaminations within the aperture. Create paper-ready figures (1-column) overplotting the Gaia DR2 catalog to the TESS Target Pixel Files. You can create plots for any target observed by TESS! Even if you do not have a TIC number, you can search by coordinates now (see examples in Github)!

Code Language(s): Python3

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tpfplotter: TESS Target Pixel File Creator

J. Lillo-Box (Aller et al., 2020, A&A, 635, 128)

tpfplotter is a user-friendly tool to create the TESS Target Pixel Files of your favorite source overplotting the aperture mask used by the SPOC pipeline and the Gaia catalogue to check for possible contaminations within the aperture. Create paper-ready figures (1-column) overplotting the Gaia DR2 catalog to the TESS Target Pixel Files. You can create plots for any target observed by TESS! Even if you do not have a TIC number, you can search by coordinates now (see examples in Github)!

About Demo
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PEXO: A Tool for Precise Modeling and Fitting for Timing, Radial Velocity and Astrometry Data

Fabo Feng, & timberhill. (2020, May 5)

PEXO is a package for making precise exoplanetology. Compared with previous models and packages, PEXO is general enough to account for binary motion and stellar reflex motions induced by planetary companions. PEXO is precise enough to treat various relativistic effects both in the Solar System and in the target system (Roemer, Shapiro, and Einstein delays). PEXO is able to model timing to a precision of 1 ns, astrometry to a precision of 1 μas, and radial velocity to a precision of 1 μm/s. There are pdf and html versions of the manual available for instructions of how to use PEXO. The fitting mode and a python wrapper are in development and expected to be released soon.

Code Language(s): R

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PEXO: A Tool for Precise Modeling and Fitting for Timing, Radial Velocity and Astrometry Data

Fabo Feng, & timberhill. (2020, May 5)

PEXO is a package for making precise exoplanetology. Compared with previous models and packages, PEXO is general enough to account for binary motion and stellar reflex motions induced by planetary companions. PEXO is precise enough to treat various relativistic effects both in the Solar System and in the target system (Roemer, Shapiro, and Einstein delays). PEXO is able to model timing to a precision of 1 ns, astrometry to a precision of 1 μas, and radial velocity to a precision of 1 μm/s. There are pdf and html versions of the manual available for instructions of how to use PEXO. The fitting mode and a python wrapper are in development and expected to be released soon.

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Exostriker: Transit and Radial velocity Interactive Fitting tool for Orbital analysis and N-body simulations

Trifon Trifonov, MPIA Heidelberg;