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).

If you've used EMAC in any part of your research, please cite our RNAAS paper either in your methods section or in the "Software used" portion of any manuscripts; see the FAQ for more information.

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.

Click here to find out about our first ever EMAC Workshop in February!
EMAC: 2302-001 EMAC 2302-001
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PyMieScatt: The Python Mie Scattering package

Sumlin, B., Heinson, W., and Chakrabarty, R.

PyMieScatt is a comprehensive forward and inverse Mie theory solver for Python 3. This package calculates relevant parameters such as absorption, scattering, extinction, asymmetry, backscatter, and more. It also contains single-line functions to calculate optical coefficients (in Mm-1) of ensembles of particles in lognormal (with single or multiple modes) or custom size distributions. The inverse calculations retrieve the complex refractive index from laboratory measurements of scattering and absorption (or backscatter), useful for studying atmospheric organic aerosol of unknown composition. Read more in our JQSRT paper!

Last updated: Feb. 7, 2023

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2302-001
2302-001

PyMieScatt: The Python Mie Scattering package

Sumlin, B., Heinson, W., and Chakrabarty, R.

PyMieScatt is a comprehensive forward and inverse Mie theory solver for Python 3. This package calculates relevant parameters such as absorption, scattering, extinction, asymmetry, backscatter, and more. It also contains single-line functions to calculate optical coefficients (in Mm-1) of ensembles of particles in lognormal (with single or multiple modes) or custom size distributions. The inverse calculations retrieve the complex refractive index from laboratory measurements of scattering and absorption (or backscatter), useful for studying atmospheric organic aerosol of unknown composition. Read more in our JQSRT paper!

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EMAC: 2301-001 EMAC 2301-001
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SHERLOCK: Searching for Hints of Exoplanets fRom Lightcurves Of spaCe-based seeKers

Dévora-Pajares, M. & Pozuelos, F. J. et al.

SHERLOCK is an end-to-end pipeline that allows the users to explore the data from space-based missions to search for planetary candidates. It can be used to recover alerted candidates by the automatic pipelines such as SPOC and the QLP, the so-called Kepler objects of interest (KOIs) and TESS objects of interest (TOIs), and to search for candidates that remain unnoticed due to detection thresholds, lack of data exploration or poor photometric quality.

Last updated: Jan. 18, 2023

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2301-001
2301-001

SHERLOCK: Searching for Hints of Exoplanets fRom Lightcurves Of spaCe-based seeKers

Dévora-Pajares, M. & Pozuelos, F. J. et al.

SHERLOCK is an end-to-end pipeline that allows the users to explore the data from space-based missions to search for planetary candidates. It can be used to recover alerted candidates by the automatic pipelines such as SPOC and the QLP, the so-called Kepler objects of interest (KOIs) and TESS objects of interest (TOIs), and to search for candidates that remain unnoticed due to detection thresholds, lack of data exploration or poor photometric quality.

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EMAC: 2212-006 EMAC 2212-006
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PACMAN: A pipeline to reduce and analyze Hubble Wide Field Camera 3 IR Grism data

Zieba, Sebastian and Kreidberg, Laura

Here we present PACMAN, an end-to-end pipeline developed to reduce and analyze HST/WFC3 data. The pipeline includes both spectral extraction and light curve fitting. The foundation of PACMAN has been already used in numerous publications (e.g., Kreidberg et al., 2014; Kreidberg et al., 2018) and these papers have already accumulated hundreds of citations.

Last updated: Dec. 27, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2212-006
2212-006

PACMAN: A pipeline to reduce and analyze Hubble Wide Field Camera 3 IR Grism data

Zieba, Sebastian and Kreidberg, Laura

Here we present PACMAN, an end-to-end pipeline developed to reduce and analyze HST/WFC3 data. The pipeline includes both spectral extraction and light curve fitting. The foundation of PACMAN has been already used in numerous publications (e.g., Kreidberg et al., 2014; Kreidberg et al., 2018) and these papers have already accumulated hundreds of citations.

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EMAC: 2212-005 EMAC 2212-005
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PHOTOe: An efficient Monte Carlo model for the slowing down of photoelectrons

A. García Muñoz

Monte Carlo model for simulating the slowing down of photoelectrons in gases in the local deposition approximation.

Version v1 works with H and He atoms plus thermal electrons. The model is described in: García Muñoz, Icarus, Volume 392, 1 March 2023, 115373

The model is available on https://antoniogarciamunoz.wordpress.com/ and upon email request from the author.

Last updated: Dec. 15, 2022

Code Language(s): fortran

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2212-005

PHOTOe: An efficient Monte Carlo model for the slowing down of photoelectrons

A. García Muñoz

Monte Carlo model for simulating the slowing down of photoelectrons in gases in the local deposition approximation.

Version v1 works with H and He atoms plus thermal electrons. The model is described in: García Muñoz, Icarus, Volume 392, 1 March 2023, 115373

The model is available on https://antoniogarciamunoz.wordpress.com/ and upon email request from the author.

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EMAC: 2212-004 EMAC 2212-004
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SpecMatch-Empirical: Spectroscopic characterization of stars with an empirical spectral library

Yee, Samuel; Petigura, Erik; von Braun, Kaspar

We present SpecMatch-Empirical, a tool for measuring the fundamental properties of stars from their spectra by comparing them against an empirical spectral library of FGKM stars. The spectral library comprises high-resolution, high signal-to-noise observed spectra from Keck/HIRES for 404 touchstone stars with well-determined stellar parameters derived from interferometry, asteroseismology, and spectrophotometry. The code achieves accuracies of 100K, 15%, and 0.09 dex in Teff, Rstar, and [Fe/H] respectively for FGKM dwarfs.

Last updated: Dec. 9, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2212-004
2212-004

SpecMatch-Empirical: Spectroscopic characterization of stars with an empirical spectral library

Yee, Samuel; Petigura, Erik; von Braun, Kaspar

We present SpecMatch-Empirical, a tool for measuring the fundamental properties of stars from their spectra by comparing them against an empirical spectral library of FGKM stars. The spectral library comprises high-resolution, high signal-to-noise observed spectra from Keck/HIRES for 404 touchstone stars with well-determined stellar parameters derived from interferometry, asteroseismology, and spectrophotometry. The code achieves accuracies of 100K, 15%, and 0.09 dex in Teff, Rstar, and [Fe/H] respectively for FGKM dwarfs.

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EMAC: 2212-003 EMAC 2212-003
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Astroquery: Library with tools to query astronomical databases

Ginsburg, Sipőcz, Brasseur et al.

Astroquery is a collection of tools for requesting data from databases hosted on remote servers with interfaces exposed on the internet, including those with web pages but without formal application program interfaces. These tools are built on the Python requests package, which is used to make HTTP requests, and astropy, which provides most of the data parsing functionality. Astroquery modules generally attempt to replicate the web page interface provided by a given service as closely as possible, making the transition from browser-based to command-line interaction easy. Astroquery enables the creation of fully reproducible workflows from data acquisition through publication.

Last updated: Dec. 7, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2212-003
2212-003

Astroquery: Library with tools to query astronomical databases

Ginsburg, Sipőcz, Brasseur et al.

Astroquery is a collection of tools for requesting data from databases hosted on remote servers with interfaces exposed on the internet, including those with web pages but without formal application program interfaces. These tools are built on the Python requests package, which is used to make HTTP requests, and astropy, which provides most of the data parsing functionality. Astroquery modules generally attempt to replicate the web page interface provided by a given service as closely as possible, making the transition from browser-based to command-line interaction easy. Astroquery enables the creation of fully reproducible workflows from data acquisition through publication.

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EMAC: 2212-002 EMAC 2212-002
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spaceKLIP: Pipeline for reducing & analyzing JWST coronagraphy data with the help of pyKLIP

JWST ERS 1386 Team

The spaceKLIP pipeline enables to reduce & analyze JWST NIRCam & MIRI coronagraphy data. It provides functions to run the official jwst stage 1 and 2 data reduction pipelines with several modifications that were made to improve the quality of high-contrast imaging reductions. It then performs PSF subtraction based on the KLIP algorithm as implemented in the widely used pyKLIP package, outputs contrast curves, and enables forward model PSF fitting for any detected companions in order to extract their properties (offset and flux). The pipeline is still under heavy development.

Last updated: Dec. 7, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2212-002
2212-002

spaceKLIP: Pipeline for reducing & analyzing JWST coronagraphy data with the help of pyKLIP

JWST ERS 1386 Team

The spaceKLIP pipeline enables to reduce & analyze JWST NIRCam & MIRI coronagraphy data. It provides functions to run the official jwst stage 1 and 2 data reduction pipelines with several modifications that were made to improve the quality of high-contrast imaging reductions. It then performs PSF subtraction based on the KLIP algorithm as implemented in the widely used pyKLIP package, outputs contrast curves, and enables forward model PSF fitting for any detected companions in order to extract their properties (offset and flux). The pipeline is still under heavy development.

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EMAC: 2211-008 EMAC 2211-008
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Butterpy: realistic star spot evolution and light curves in Python

Claytor, Zachary R. et al.

Butterpy is a Python-based tool for simulating star spot emergence, evolution, and decay as well as stellar rotational light curves. It is adapted from the physically motivated model used by Aigrain et al. (2015, MNRAS, 450, 3211) to test the recovery of stellar rotation periods using different frequency analysis techniques. Butterpy allows the user to simulate light curves of stars with variable activity level, rotation period, spot lifetime, magnetic cycle duration and overlap, spot emergence latitudes, and latitudinal differential rotation shear. The name Butterpy is a portmanteau of "butterfly" (like the solar butterfly diagram) and "Python."

Last updated: Nov. 30, 2022

Code Language(s): Python 3

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https://emac.gsfc.nasa.gov?cid=2211-008
2211-008

Butterpy: realistic star spot evolution and light curves in Python

Claytor, Zachary R. et al.

Butterpy is a Python-based tool for simulating star spot emergence, evolution, and decay as well as stellar rotational light curves. It is adapted from the physically motivated model used by Aigrain et al. (2015, MNRAS, 450, 3211) to test the recovery of stellar rotation periods using different frequency analysis techniques. Butterpy allows the user to simulate light curves of stars with variable activity level, rotation period, spot lifetime, magnetic cycle duration and overlap, spot emergence latitudes, and latitudinal differential rotation shear. The name Butterpy is a portmanteau of "butterfly" (like the solar butterfly diagram) and "Python."

EMAC: 2211-007 EMAC 2211-007
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Kiauhoku: Python utilities for stellar model grid interpolation

Claytor, Zachary R. et al.

Kiauhoku is a Python package for interacting with, interpolating, and fitting stellar evolutionary tracks to observational data. It includes popular stellar model grids like MIST, Dartmouth, and GARSTEC, as well as a few custom YREC grids, with more being added over time.
From Hawaiian:
 1. vt. To sense the span of a star's existence (i.e., its age).
 2. n. The speed of a star (in this case, its rotational speed).
This name was created in partnership with Dr. Larry Kimura and Bruce Torres Fischer, a student participant in A Hua He Inoa, a program to bring Hawaiian naming practices to new astronomical discoveries. We are grateful for their collaboration.

Last updated: Nov. 30, 2022

Code Language(s): Python 3

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https://emac.gsfc.nasa.gov?cid=2211-007
2211-007

Kiauhoku: Python utilities for stellar model grid interpolation

Claytor, Zachary R. et al.

Kiauhoku is a Python package for interacting with, interpolating, and fitting stellar evolutionary tracks to observational data. It includes popular stellar model grids like MIST, Dartmouth, and GARSTEC, as well as a few custom YREC grids, with more being added over time.
From Hawaiian:
 1. vt. To sense the span of a star's existence (i.e., its age).
 2. n. The speed of a star (in this case, its rotational speed).
This name was created in partnership with Dr. Larry Kimura and Bruce Torres Fischer, a student participant in A Hua He Inoa, a program to bring Hawaiian naming practices to new astronomical discoveries. We are grateful for their collaboration.

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EMAC: 2211-006 EMAC 2211-006
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rfast: A fast tool for planetary spectral forward and inverse modeling.

T. Robinson

The rfast tool is an ultra-quick planetary spectrum simulator and remote sensing tool, originally designed for rapid retrieval explorations for mission concept studies. Through a convenient runscript, users can generate a noise-free spectrum of a planetary environment, add instrumental noise, and perform inverse modeling. The rfast tool is capable of applications to simulated and real observations spanning reflected-light, thermal emission, and transit transmission.

Last updated: Nov. 21, 2022

Code Language(s): Python

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2211-006

rfast: A fast tool for planetary spectral forward and inverse modeling.

T. Robinson

The rfast tool is an ultra-quick planetary spectrum simulator and remote sensing tool, originally designed for rapid retrieval explorations for mission concept studies. Through a convenient runscript, users can generate a noise-free spectrum of a planetary environment, add instrumental noise, and perform inverse modeling. The rfast tool is capable of applications to simulated and real observations spanning reflected-light, thermal emission, and transit transmission.

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EMAC: 2211-005 EMAC 2211-005
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Molecfit: A general tool for telluric absorption correction

Smette et al., Kausch et al.

Molecfit is a tool to correct for telluric absorption lines based on synthetic modelling of the Earth’s atmospheric transmission. It can be used with data obtained with various ground-based telescopes and instruments. It combines a publicly available radiative transfer code, a molecular line database, atmospheric profiles, and various kernels to model the instrument LSF. The atmospheric profiles are created by merging a standard atmospheric profile representative of a given observatory’s climate, of local meteorological data, and of dynamically retrieved altitude profiles for temperature, pressure, and humidity.

Last updated: Nov. 18, 2022

Code Language(s): C, ESO Common Pipeline Library (CPL), Python

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2211-005

Molecfit: A general tool for telluric absorption correction

Smette et al., Kausch et al.

Molecfit is a tool to correct for telluric absorption lines based on synthetic modelling of the Earth’s atmospheric transmission. It can be used with data obtained with various ground-based telescopes and instruments. It combines a publicly available radiative transfer code, a molecular line database, atmospheric profiles, and various kernels to model the instrument LSF. The atmospheric profiles are created by merging a standard atmospheric profile representative of a given observatory’s climate, of local meteorological data, and of dynamically retrieved altitude profiles for temperature, pressure, and humidity.

EMAC: 2211-004 EMAC 2211-004
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https://emac.gsfc.nasa.gov?cid=2211-004
Pyshellspec: Binary systems with circumstellar matter (β Lyrae)

Brož, M., Nemravová, J.

PYSHELLSPEC is an astrophysical tool for modeling of binary systems with circumstellar matter (e.g. accretion disk, jet, shell), computation of interferometric observables |V2|, arg T3, |T3|, |dV|, arg dV, comparison of light curves, spectro-interferometry, spectra, and SED with observations, and both global and local optimisation of system parameters. It is based on Shellspec, a long-characteristic LTE radiation transfer code by Budaj & Richards (2004).

Last updated: Nov. 17, 2022

Code Language(s): Python3, Fortran

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https://emac.gsfc.nasa.gov?cid=2211-004
2211-004

Pyshellspec: Binary systems with circumstellar matter (β Lyrae)

Brož, M., Nemravová, J.

PYSHELLSPEC is an astrophysical tool for modeling of binary systems with circumstellar matter (e.g. accretion disk, jet, shell), computation of interferometric observables |V2|, arg T3, |T3|, |dV|, arg dV, comparison of light curves, spectro-interferometry, spectra, and SED with observations, and both global and local optimisation of system parameters. It is based on Shellspec, a long-characteristic LTE radiation transfer code by Budaj & Richards (2004).

EMAC: 2211-003 EMAC 2211-003
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AnalyticLC: An Accurate 3D Analytic Modeling Tool for Exoplanetary Photometry, Radial Velocity and Astrometry

Yair Judkovsky, Aviv Ofir and Oded Aharonson

AnalyticLC is an accurate photometry, radial velocity, and astrometry modeling tool. It is based on a fourth-order expansion of the Disturbing Function, incorporating 3D orbital dynamics. The analytic approach of AnalyticLC elucidates the relation between orbital dynamics and observable quantities. In addition, it offers advantages for analyzing observations with a long time span, a scenario becoming increasingly common in this era of multiple space missions. AnalyticLC has been used to interpret Kepler data and obtain estimates of more than a hundred exoplanets' physical and orbital properties.

Last updated: Nov. 14, 2022

Code Language(s): Matlab

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https://emac.gsfc.nasa.gov?cid=2211-003
2211-003

AnalyticLC: An Accurate 3D Analytic Modeling Tool for Exoplanetary Photometry, Radial Velocity and Astrometry

Yair Judkovsky, Aviv Ofir and Oded Aharonson

AnalyticLC is an accurate photometry, radial velocity, and astrometry modeling tool. It is based on a fourth-order expansion of the Disturbing Function, incorporating 3D orbital dynamics. The analytic approach of AnalyticLC elucidates the relation between orbital dynamics and observable quantities. In addition, it offers advantages for analyzing observations with a long time span, a scenario becoming increasingly common in this era of multiple space missions. AnalyticLC has been used to interpret Kepler data and obtain estimates of more than a hundred exoplanets' physical and orbital properties.

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EMAC: 2211-002 EMAC 2211-002
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Optimal BLS: Optimally-efficient code for transit searches in long time series

Aviv Ofir

Explicitly including Keplerian dynamics in the transit search allows Optimal BLS to enhance transit detectability while allowing such searches to be done with much-reduced resources and time. By using the (standard) BLS, one is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). Physical system parameters, such as the host star's size and mass, directly affect transit search. This understanding can then be used to optimize the search for every star individually. The code is well-used by the community.

Last updated: Nov. 14, 2022

Code Language(s): Matlab, Octave

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https://emac.gsfc.nasa.gov?cid=2211-002
2211-002

Optimal BLS: Optimally-efficient code for transit searches in long time series

Aviv Ofir

Explicitly including Keplerian dynamics in the transit search allows Optimal BLS to enhance transit detectability while allowing such searches to be done with much-reduced resources and time. By using the (standard) BLS, one is either fairly insensitive to long-period planets or less sensitive to short-period planets and computationally slower by a significant factor of ~330 (for a 3 yr long dataset). Physical system parameters, such as the host star's size and mass, directly affect transit search. This understanding can then be used to optimize the search for every star individually. The code is well-used by the community.

EMAC: 2211-001 EMAC 2211-001
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pyPplusS: Fast and precise light-curve model for transiting exoplanets with rings

Eden Rein and Aviv Ofir

The Polygon + Segments model allows modeling the light curve of an exoplanet with rings. This high-precision model includes full ring geometry as well as possible ring transparency and the host star’s limb darkening. Additionally, it can model oblate ringless planets as an opaque “ring” (same shape as a planet). pyPplusS is also computationally efficient, requiring just a 1D integration over a small range, making it faster than existing techniques. The algorithm at its core is further generalized to compute the light curve of any set of convex primitive shapes in transit (e.g. multiple planets, oblate planets, moons, rings, combination thereof, etc.) while accounting for their overlaps.

Last updated: Nov. 14, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2211-001
2211-001

pyPplusS: Fast and precise light-curve model for transiting exoplanets with rings

Eden Rein and Aviv Ofir

The Polygon + Segments model allows modeling the light curve of an exoplanet with rings. This high-precision model includes full ring geometry as well as possible ring transparency and the host star’s limb darkening. Additionally, it can model oblate ringless planets as an opaque “ring” (same shape as a planet). pyPplusS is also computationally efficient, requiring just a 1D integration over a small range, making it faster than existing techniques. The algorithm at its core is further generalized to compute the light curve of any set of convex primitive shapes in transit (e.g. multiple planets, oblate planets, moons, rings, combination thereof, etc.) while accounting for their overlaps.

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EMAC: 2210-005 EMAC 2210-005
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PySME: Stellar Spectral Synthesis and Parameter Fitting

Wehrhahn, A., Piskunov, N., Valenti, J.

Spectroscopy Made Easy (SME) is a software tool that fits an observed spectrum of a star with a model spectrum. Since its initial release in 1996, SME has been a suite of IDL routines that call a dynamically linked library, which is compiled from C++ and Fortran. This classic IDL version of SME is available for download. In 2018, we began began reimplementing the IDL part of SME in python 3, adopting an object oriented paradigm and continuous integration practices (code repository, build automation, self-testing, frequent builds).

Last updated: Oct. 25, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2210-005
2210-005

PySME: Stellar Spectral Synthesis and Parameter Fitting

Wehrhahn, A., Piskunov, N., Valenti, J.

Spectroscopy Made Easy (SME) is a software tool that fits an observed spectrum of a star with a model spectrum. Since its initial release in 1996, SME has been a suite of IDL routines that call a dynamically linked library, which is compiled from C++ and Fortran. This classic IDL version of SME is available for download. In 2018, we began began reimplementing the IDL part of SME in python 3, adopting an object oriented paradigm and continuous integration practices (code repository, build automation, self-testing, frequent builds).

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EMAC: 2210-004 EMAC 2210-004
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ATOCA: Algorithm to Treat Order Contamination. Used to decontaminate and extract spectra from image.

Antoine Darveau-Bernier et al.

ATOCA is used to extract and decontaminate spectroscopic images with multiple sources or diffraction orders. The inputs are, for all orders and sources: the wavelength solutions, the trace profiles, the throughputs and the spectral resolution kernels. From this, ATOCA can model simultaneously the detector and extract the spectra. See Darveau-Bernier et al. (2022) for more details.

Last updated: Oct. 14, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2210-004
2210-004

ATOCA: Algorithm to Treat Order Contamination. Used to decontaminate and extract spectra from image.

Antoine Darveau-Bernier et al.

ATOCA is used to extract and decontaminate spectroscopic images with multiple sources or diffraction orders. The inputs are, for all orders and sources: the wavelength solutions, the trace profiles, the throughputs and the spectral resolution kernels. From this, ATOCA can model simultaneously the detector and extract the spectra. See Darveau-Bernier et al. (2022) for more details.

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EMAC: 2210-003 EMAC 2210-003
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FORECAST: Finely Optimised REtrieval of Companions of Accelerating STars

M. Bonavita, R. Gratton, C. Fontanive, A. Sozzetti

A significant proper motion difference between two catalogues for a given star is a good indication of the presence of a perturbing body. FORECAST allows you to identify the region where a companion compatible with the measured Δμ should appear if the star is directly imaged. It also provides an estimate of the mass of the companion compatible with the astrometric signal at each position in the allowed region. FORECAST maps can be used both to identify and confirm potential direct imaged sub-stellar candidates.

Last updated: Oct. 10, 2022

Code Language(s): N/A

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https://emac.gsfc.nasa.gov?cid=2210-003
2210-003

FORECAST: Finely Optimised REtrieval of Companions of Accelerating STars

M. Bonavita, R. Gratton, C. Fontanive, A. Sozzetti

A significant proper motion difference between two catalogues for a given star is a good indication of the presence of a perturbing body. FORECAST allows you to identify the region where a companion compatible with the measured Δμ should appear if the star is directly imaged. It also provides an estimate of the mass of the companion compatible with the astrometric signal at each position in the allowed region. FORECAST maps can be used both to identify and confirm potential direct imaged sub-stellar candidates.

EMAC: 2210-002 EMAC 2210-002
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https://emac.gsfc.nasa.gov?cid=2210-002
Exo-DMC: Exoplanet Detection Map Calculator

M. Bonavita, Silvano Desidera, Ernst de Moij, Arthur Vigan, Justine Lannier

Exo-DMC (Exoplanet Detection Map Calculator) is a Monte Carlo tool for the statistical analysis of exoplanet surveys results. It combines the information on the target stars with the instrument detection limits to estimate the probability of detection of a given synthetic planet population, ultimately generating detection probability maps. The Exo-DMC is the latest (although the first one in Python) rendition of the MESS (Multi-purpose Exoplanet Simulation System). Like MESS, the DMC allows for a high level of flexibility in terms of possible assumptions on the synthetic planet population to be used for the determination of the detection probability.

Last updated: Oct. 10, 2022

Code Language(s): python3

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https://emac.gsfc.nasa.gov?cid=2210-002
2210-002

Exo-DMC: Exoplanet Detection Map Calculator

M. Bonavita, Silvano Desidera, Ernst de Moij, Arthur Vigan, Justine Lannier

Exo-DMC (Exoplanet Detection Map Calculator) is a Monte Carlo tool for the statistical analysis of exoplanet surveys results. It combines the information on the target stars with the instrument detection limits to estimate the probability of detection of a given synthetic planet population, ultimately generating detection probability maps. The Exo-DMC is the latest (although the first one in Python) rendition of the MESS (Multi-purpose Exoplanet Simulation System). Like MESS, the DMC allows for a high level of flexibility in terms of possible assumptions on the synthetic planet population to be used for the determination of the detection probability.

EMAC: 2210-001 EMAC 2210-001
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https://emac.gsfc.nasa.gov?cid=2210-001
blase: An Interpretable Machine Learning Framework for Modeling High-Resolution Spectroscopic Data

Michael Gully-Santiago & Caroline V. Morley

Blasé introduces a powerful new approach to whole-spectrum fitting: clone 10,000+ spectral lines from a precomputed synthetic spectral model template, and then learn the perturbations to those lines through comparison to real data. Each spectral line has 4 parameters, yielding possibly 40,000+ parameters. The technique hinges on the magic of autodiff, the enabling technology behind Machine Learning, to tune all of those parameters precisely and quickly. The tool has conceivable extensions to Doppler imaging, Precision RV's, abundances, and more. It is built in PyTorch, with native GPU support.

Last updated: Oct. 10, 2022

Code Language(s): Python, PyTorch

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https://emac.gsfc.nasa.gov?cid=2210-001
2210-001

blase: An Interpretable Machine Learning Framework for Modeling High-Resolution Spectroscopic Data

Michael Gully-Santiago & Caroline V. Morley

Blasé introduces a powerful new approach to whole-spectrum fitting: clone 10,000+ spectral lines from a precomputed synthetic spectral model template, and then learn the perturbations to those lines through comparison to real data. Each spectral line has 4 parameters, yielding possibly 40,000+ parameters. The technique hinges on the magic of autodiff, the enabling technology behind Machine Learning, to tune all of those parameters precisely and quickly. The tool has conceivable extensions to Doppler imaging, Precision RV's, abundances, and more. It is built in PyTorch, with native GPU support.

About Demo
EMAC: 2209-015 EMAC 2209-015
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https://emac.gsfc.nasa.gov?cid=2209-015
SysSimPyPlots: Loading, analyzing, and plotting catalogs generated from the SysSim models

Matthias Y. He

SysSimPyPlots is a Python package for loading, plotting, and otherwise visualizing the simulated catalogs generated from ExoplanetsSysSim, a comprehensive forward modeling framework for studying planetary systems based on the Kepler mission. In particular, it is designed to work with the SysSim clustered planetary system models (https://github.com/ExoJulia/SysSimExClusters) that characterize the underlying occurrence and intra-system correlations of multi-planet systems.

Last updated: Sep. 30, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2209-015
2209-015

SysSimPyPlots: Loading, analyzing, and plotting catalogs generated from the SysSim models

Matthias Y. He

SysSimPyPlots is a Python package for loading, plotting, and otherwise visualizing the simulated catalogs generated from ExoplanetsSysSim, a comprehensive forward modeling framework for studying planetary systems based on the Kepler mission. In particular, it is designed to work with the SysSim clustered planetary system models (https://github.com/ExoJulia/SysSimExClusters) that characterize the underlying occurrence and intra-system correlations of multi-planet systems.

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EMAC: 2209-014 EMAC 2209-014
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SysSimPyMMEN: Inferring Minimum-Mass Extrasolar Nebulae from the SysSim models

Matthias Y. He

SysSimPyMMEN is a Python package for inferring the minimum-mass extrasolar nebula (MMEN), a power-law profile for the minimum mass in disk solids required to form the existing exoplanets if they formed in their present locations. It is designed to work with the SysSim clustered planetary system models (https://github.com/ExoJulia/SysSimExClusters) that characterize the underlying occurrence and intra-system correlations of multi-planet systems, but can be easily applied to any other planetary system by the user.

Last updated: Sep. 30, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2209-014
2209-014

SysSimPyMMEN: Inferring Minimum-Mass Extrasolar Nebulae from the SysSim models

Matthias Y. He

SysSimPyMMEN is a Python package for inferring the minimum-mass extrasolar nebula (MMEN), a power-law profile for the minimum mass in disk solids required to form the existing exoplanets if they formed in their present locations. It is designed to work with the SysSim clustered planetary system models (https://github.com/ExoJulia/SysSimExClusters) that characterize the underlying occurrence and intra-system correlations of multi-planet systems, but can be easily applied to any other planetary system by the user.

About
EMAC: 2209-013 EMAC 2209-013
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https://emac.gsfc.nasa.gov?cid=2209-013
RAPOC: Rosseland And Planck Opacity Converter

Lorenzo V. Mugnai and Darius Modirrousta-Galian

RAPOC (Rosseland and Planck Opacity Converter) is a Python 3 code that calculates Rosseland and Planck mean opacities from wavelength-dependent opacities for a given temperature, pressure, and wavelength range. In addition to being user-friendly and rapid, RAPOC can interpolate between discrete data points, making it flexible and widely applicable to the astrophysical and Earth-sciences fields, as well as in engineering. For the input data, RAPOC can use ExoMol and DACE data, or any user-defined data, provided that it is in a readable format.

Last updated: Sep. 30, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2209-013
2209-013

RAPOC: Rosseland And Planck Opacity Converter

Lorenzo V. Mugnai and Darius Modirrousta-Galian

RAPOC (Rosseland and Planck Opacity Converter) is a Python 3 code that calculates Rosseland and Planck mean opacities from wavelength-dependent opacities for a given temperature, pressure, and wavelength range. In addition to being user-friendly and rapid, RAPOC can interpolate between discrete data points, making it flexible and widely applicable to the astrophysical and Earth-sciences fields, as well as in engineering. For the input data, RAPOC can use ExoMol and DACE data, or any user-defined data, provided that it is in a readable format.

About
EMAC: 2209-012 EMAC 2209-012
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https://emac.gsfc.nasa.gov?cid=2209-012
Kamodo: a CCMC tool for access, interpolation, and visualization of data in python.

The Community Coordinated Modeling Center at NASA GSFC

Kamodo allows model developers to represent simulation results as mathematical functions which may be manipulated directly by end users. Kamodo handles unit conversion transparently and supports interactive science discovery through jupyter notebooks with minimal coding and is accessible through python.

Last updated: Sep. 26, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2209-012
2209-012

Kamodo: a CCMC tool for access, interpolation, and visualization of data in python.

The Community Coordinated Modeling Center at NASA GSFC

Kamodo allows model developers to represent simulation results as mathematical functions which may be manipulated directly by end users. Kamodo handles unit conversion transparently and supports interactive science discovery through jupyter notebooks with minimal coding and is accessible through python.

About
EMAC: 2209-011 EMAC 2209-011
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https://emac.gsfc.nasa.gov?cid=2209-011
ECLIPS3D: Public code for linear wave and circulation calculations

F. Debras et al.

Public Fortran 90 code for linear wave and circulation calculations, developed originally for planetary atmospheres, with python scripts provided for data analysis.4 setups are provided: 2D_axi: eigenvector setup in spherical coordinates assuming axisymmetry around the axis of rotation. A longitudinal wavenumber, m, must therefore be provided. 2D_shallow: eigenvector setup for shallow water beta-plane. The latitude of the beta plane and characteristic height can be changed. 3D: eigenvector setup in full 3D, spherical coordinates. 3D_steady: linear circulation setup, hence matrix inversion. A forcing and a dissipation have to be implemented for a linear steady state to exist.

Last updated: Sep. 26, 2022

Code Language(s): Fortran

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https://emac.gsfc.nasa.gov?cid=2209-011
2209-011

ECLIPS3D: Public code for linear wave and circulation calculations

F. Debras et al.

Public Fortran 90 code for linear wave and circulation calculations, developed originally for planetary atmospheres, with python scripts provided for data analysis.4 setups are provided: 2D_axi: eigenvector setup in spherical coordinates assuming axisymmetry around the axis of rotation. A longitudinal wavenumber, m, must therefore be provided. 2D_shallow: eigenvector setup for shallow water beta-plane. The latitude of the beta plane and characteristic height can be changed. 3D: eigenvector setup in full 3D, spherical coordinates. 3D_steady: linear circulation setup, hence matrix inversion. A forcing and a dissipation have to be implemented for a linear steady state to exist.

About
EMAC: 2209-010 EMAC 2209-010
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https://emac.gsfc.nasa.gov?cid=2209-010
TESS-SIP: TESS Systematics Insensitive Periodogram

Hedges et al.

Tool for creating a Systematics-insensitive Periodogram (SIP) to detect long period rotation in NASA's TESS mission data. Read more in our published Research Note of the American Astronomical Society. SIP is a method of detrending telescope systematics (the TESS scattered light) simultaneously with calculating a Lomb-Scargle periodogram. This allows us to estimate of the rotation rate of variables with a period of >30days when there are multiple sectors. You can read a more in-depth work of how SIP is used in NASA's Kepler/K2 data here.

Last updated: Sep. 26, 2022

Code Language(s):

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https://emac.gsfc.nasa.gov?cid=2209-010
2209-010

TESS-SIP: TESS Systematics Insensitive Periodogram

Hedges et al.

Tool for creating a Systematics-insensitive Periodogram (SIP) to detect long period rotation in NASA's TESS mission data. Read more in our published Research Note of the American Astronomical Society. SIP is a method of detrending telescope systematics (the TESS scattered light) simultaneously with calculating a Lomb-Scargle periodogram. This allows us to estimate of the rotation rate of variables with a period of >30days when there are multiple sectors. You can read a more in-depth work of how SIP is used in NASA's Kepler/K2 data here.

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EMAC: 2209-009 EMAC 2209-009
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https://emac.gsfc.nasa.gov?cid=2209-009
IGRINS RV: A Radial Velocity Pipeline for IGRINS

Stahl, A. et al. 2021; Tang, S-Y. et al. 2021

IGRINS RV is a python open source pipeline for extracting radial velocities (RVs) from spectra taken with the Immersion GRating INfrared Spectrometer (IGRINS). It uses a modified forward modeling technique that leverages telluric absorption lines as a common-path wavelength calibrator. IGRINS RV achieves an RV precision in the H and K bands of around 25-30 m/s for narrow-line stars, and it has successfully recovered the planet-induced RV signals of both HD 189733 and τ Boo A. Visit Stahl et al. 2021 to see the published paper.

Last updated: Sep. 26, 2022

Code Language(s):

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https://emac.gsfc.nasa.gov?cid=2209-009
2209-009

IGRINS RV: A Radial Velocity Pipeline for IGRINS

Stahl, A. et al. 2021; Tang, S-Y. et al. 2021

IGRINS RV is a python open source pipeline for extracting radial velocities (RVs) from spectra taken with the Immersion GRating INfrared Spectrometer (IGRINS). It uses a modified forward modeling technique that leverages telluric absorption lines as a common-path wavelength calibrator. IGRINS RV achieves an RV precision in the H and K bands of around 25-30 m/s for narrow-line stars, and it has successfully recovered the planet-induced RV signals of both HD 189733 and τ Boo A. Visit Stahl et al. 2021 to see the published paper.

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EMAC: 2209-008 EMAC 2209-008
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https://emac.gsfc.nasa.gov?cid=2209-008
AccretR: A planetary accretion and composition code in R

Mohit Melwani Daswani

AccretR is a planetary body accretion Monte Carlo code that calculates mass, radius and bulk composition along a specified growth track, for orderly/hierarchical, runaway, and random particle accretion models, optimized for icy ocean worlds in our Solar System (priors can be modified for other systems). Elements in the model include concentrations of: H, C, N, O, Na, Mg, Al, Si, S, Cl, K, Ca, and Fe. Maximal water is also computed, assuming all H goes into forming water. Accretional heat is also calculated.

Last updated: Sep. 26, 2022

Code Language(s): R

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https://emac.gsfc.nasa.gov?cid=2209-008
2209-008

AccretR: A planetary accretion and composition code in R

Mohit Melwani Daswani

AccretR is a planetary body accretion Monte Carlo code that calculates mass, radius and bulk composition along a specified growth track, for orderly/hierarchical, runaway, and random particle accretion models, optimized for icy ocean worlds in our Solar System (priors can be modified for other systems). Elements in the model include concentrations of: H, C, N, O, Na, Mg, Al, Si, S, Cl, K, Ca, and Fe. Maximal water is also computed, assuming all H goes into forming water. Accretional heat is also calculated.

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EMAC: 2209-007 EMAC 2209-007
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https://emac.gsfc.nasa.gov?cid=2209-007
FastChem: Ultra-fast Equilibrium Chemistry

Daniel Kitzmann, Joachim Stock

FastChem 2 is a new version of the established semi-analytical thermochemical equilibrium code FastChem. Whereas the original version of FastChem is limited to atmospheres containing a significant amount of the element hydrogen, FastChem 2 is now also applicable to chemical mixtures dominated by any other species such as CO2, N2, or Si for example. The code is written in object-oriented C++ and also offers an optional Python module.

Last updated: Sep. 26, 2022

Code Language(s): C++, Python3

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https://emac.gsfc.nasa.gov?cid=2209-007
2209-007

FastChem: Ultra-fast Equilibrium Chemistry

Daniel Kitzmann, Joachim Stock

FastChem 2 is a new version of the established semi-analytical thermochemical equilibrium code FastChem. Whereas the original version of FastChem is limited to atmospheres containing a significant amount of the element hydrogen, FastChem 2 is now also applicable to chemical mixtures dominated by any other species such as CO2, N2, or Si for example. The code is written in object-oriented C++ and also offers an optional Python module.

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EMAC: 2209-006 EMAC 2209-006
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https://emac.gsfc.nasa.gov?cid=2209-006
Staralt: Calculating Object Visibility for Ground-based Telescopes

Isaac Newton Group of Telescopes

Staralt is a program that shows the observability of objects in various ways: either you can plot altitude against time for a particular night (Staralt), or plot the path of your objects across the sky for a particular night (Startrack), or plot how altitude changes over a year (Starobs), or get a table with the best observing date for each object (Starmult).

Last updated: Sep. 26, 2022

Code Language(s): PhP, Fortran

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https://emac.gsfc.nasa.gov?cid=2209-006
2209-006

Staralt: Calculating Object Visibility for Ground-based Telescopes

Isaac Newton Group of Telescopes

Staralt is a program that shows the observability of objects in various ways: either you can plot altitude against time for a particular night (Staralt), or plot the path of your objects across the sky for a particular night (Startrack), or plot how altitude changes over a year (Starobs), or get a table with the best observing date for each object (Starmult).

EMAC: 2209-005 EMAC 2209-005
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https://emac.gsfc.nasa.gov?cid=2209-005
ExoAtmospheres: IAC community database for exoplanet atmospheric observations

The Exoplanets and Astrobiology group at IAC

The Exoplanet Atmospheres Database is built for the community and maintained by the community. Exoplanet atmospheres is an exciting and vibrant field of research, where new discoveries and publications occur at a very fast pace, and it is easy to miss many interesting results. The main purpose of this database is to become a quick and useful repository of all available exoplanet atmospheres observations, and also to help in the gathering of useful references for a given planet or planet types.

Last updated: Sep. 26, 2022

Code Language(s): php

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https://emac.gsfc.nasa.gov?cid=2209-005
2209-005

ExoAtmospheres: IAC community database for exoplanet atmospheric observations

The Exoplanets and Astrobiology group at IAC

The Exoplanet Atmospheres Database is built for the community and maintained by the community. Exoplanet atmospheres is an exciting and vibrant field of research, where new discoveries and publications occur at a very fast pace, and it is easy to miss many interesting results. The main purpose of this database is to become a quick and useful repository of all available exoplanet atmospheres observations, and also to help in the gathering of useful references for a given planet or planet types.

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EMAC: 2207-132 EMAC 2207-132
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https://emac.gsfc.nasa.gov?cid=2207-132
THAI: TRAPPIST Habitable Atmosphere Intercomparison GCM Data Repository

THAI Team (T. Fauchez et al.)

The TRAPPIST Habitable Atmosphere Intercomparison (THAI) project is a model inter-comparison effort between four GCMs: ExoCAM, LMD-G, ROCKE3D and the UM – examining a single interesting test case (TRAPPIST-1e) under several different atmosphere scenarios. The CKAN data repository provides NetCDF files for each case, allowing for examination and intercomparison of results from the different models. Scripts to process the data and plot them are available on our Github repository.

Last updated: Sep. 22, 2022

Code Language(s): N/A

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https://emac.gsfc.nasa.gov?cid=2207-132
2207-132

THAI: TRAPPIST Habitable Atmosphere Intercomparison GCM Data Repository

THAI Team (T. Fauchez et al.)

The TRAPPIST Habitable Atmosphere Intercomparison (THAI) project is a model inter-comparison effort between four GCMs: ExoCAM, LMD-G, ROCKE3D and the UM – examining a single interesting test case (TRAPPIST-1e) under several different atmosphere scenarios. The CKAN data repository provides NetCDF files for each case, allowing for examination and intercomparison of results from the different models. Scripts to process the data and plot them are available on our Github repository.

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EMAC: 2209-004 EMAC 2209-004
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https://emac.gsfc.nasa.gov?cid=2209-004
The TESS Triple-9 (TT9) Catalog: 999 uniformly vetted TESS candidate exoplanets

Luca Cacciapuoti et al.

The TESS Triple 9 Catalog (TT9, in short) contains dispositions for 999 candidate exoplanetary signals detected by the Transiting Exoplanet Survey Satellite (TESS). These signals were classified as Planet Candidate (PC), False Positive (FP) or Potential False Positive (PFP) based on TESS data (both light curves and images) as well as ancillary information such as stellar catalog. The data has been analyzed using the DAVE pipeline (https://github.com/exoplanetvetting/DAVE) and with the aid of Citizen Science, through the NASA funded Planet Patrol project (https://www.zooniverse.org/projects/marckuchner/planet-patrol). DAVE pdf products are available on https://exofop.ipac.caltech.edu/tess

Last updated: Sep. 22, 2022

Code Language(s): Python

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https://emac.gsfc.nasa.gov?cid=2209-004
2209-004

The TESS Triple-9 (TT9) Catalog: 999 uniformly vetted TESS candidate exoplanets

Luca Cacciapuoti et al.

The TESS Triple 9 Catalog (TT9, in short) contains dispositions for 999 candidate exoplanetary signals detected by the Transiting Exoplanet Survey Satellite (TESS). These signals were classified as Planet Candidate (PC), False Positive (FP) or Potential False Positive (PFP) based on TESS data (both light curves and images) as well as ancillary information such as stellar catalog. The data has been analyzed using the DAVE pipeline (https://github.com/exoplanetvetting/DAVE) and with the aid of Citizen Science, through the NASA funded Planet Patrol project (https://www.zooniverse.org/projects/marckuchner/planet-patrol). DAVE pdf products are available on https://exofop.ipac.caltech.edu/tess

EMAC: 2209-003 EMAC 2209-003
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https://emac.gsfc.nasa.gov?cid=2209-003
Prose: A Python framework for modular astronomical images processing

Lionel Garcia

Built for astronomy, Prose is instrument-agnostic and allows the construction of data reduction pipelines using a wide range of building blocks, pre-implemented or user-defined. Using its modular architecture, it features basic reduction pipelines to deal with common tasks such as automatic reduction and photometric extraction.

Last updated: Sep. 21, 2022

Code Language(s): Python3, LaTeX

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https://emac.gsfc.nasa.gov?cid=2209-003
2209-003

Prose: A Python framework for modular astronomical images processing

Lionel Garcia

Built for astronomy, Prose is instrument-agnostic and allows the construction of data reduction pipelines using a wide range of building blocks, pre-implemented or user-defined. Using its modular architecture, it features basic reduction pipelines to deal with common tasks such as automatic reduction and photometric extraction.

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EMAC: 2209-002 EMAC 2209-002
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https://emac.gsfc.nasa.gov?cid=2209-002
exoVista: Planetary System Models for Survey Analyses

Christopher Stark

ExoVista quickly produces a large number of planetary system models that can serve as a standard set for simulated study with the direct imaging, transit, astrometric, interferometric, and astrometric methods at scattered light wavelengths. ExoVista distributes planets consistent with the Kepler occurrence rates around Hipparcos stars within 50 pc, assigns a mass, radius, and albedo to each planet, checks for stability of the orbits, evolves all objects with a gravitational n-body integrator, and generates a quasi-self-consistent debris disk for each system consistent with the LBTI HOSTS exozodi survey.

Last updated: Sep. 15, 2022

Code Language(s): IDL, C

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https://emac.gsfc.nasa.gov?cid=2209-002
2209-002

exoVista: Planetary System Models for Survey Analyses

Christopher Stark

ExoVista quickly produces a large number of planetary system models that can serve as a standard set for simulated study with the direct imaging, transit, astrometric, interferometric, and astrometric methods at scattered light wavelengths. ExoVista distributes planets consistent with the Kepler occurrence rates around Hipparcos stars within 50 pc, assigns a mass, radius, and albedo to each planet, checks for stability of the orbits, evolves all objects with a gravitational n-body integrator, and generates a quasi-self-consistent debris disk for each system consistent with the LBTI HOSTS exozodi survey.

EMAC: 2209-001 EMAC 2209-001
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https://emac.gsfc.nasa.gov?cid=2209-001
M_-M_K-: Estimating realistic stellar masses from magnitudes

Andrew Mann et al.

Code converts absolute 2MASS Ks-band magnitude (or a distance and a Ks-band magnitude) into an estimate of the stellar mass using the empirical relation derived from the resolved photometry and orbits of astrometric binaries. The code outputs errors based on the relationship's scatter and errors in the provided distance and Ks magnitude.

Last updated: Sep. 15, 2022

Code Language(s): Python, IDL

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https://emac.gsfc.nasa.gov?cid=2209-001
2209-001

M_-M_K-: Estimating realistic stellar masses from magnitudes

Andrew Mann et al.

Code converts absolute 2MASS Ks-band magnitude (or a distance and a Ks-band magnitude) into an estimate of the stellar mass using the empirical relation derived from the resolved photometry and orbits of astrometric binaries. The code outputs errors based on the relationship's scatter and errors in the provided distance and Ks magnitude.

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EMAC: 2208-002 EMAC 2208-002
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https://emac.gsfc.nasa.gov?cid=2208-002
DustPy: A Python Package for Dust Evolution in Protoplanetary Disks

Sebastian Markus Stammler ; Tilman Birnstiel

DustPy simulates the radial evolution of gas and dust in protoplanetary disks, including viscous evolution of the gas, advection and diffusion of the dust, as well as dust growth by solving the Smoluchowski equation.

Last updated: Aug. 23, 2022

Code Language(s): Python3, Fortran

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https://emac.gsfc.nasa.gov?cid=2208-002
2208-002

DustPy: A Python Package for Dust Evolution in Protoplanetary Disks

Sebastian Markus Stammler ; Tilman Birnstiel

DustPy simulates the radial evolution of gas and dust in protoplanetary disks, including viscous evolution of the gas, advection and diffusion of the dust, as well as dust growth by solving the Smoluchowski equation.

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EMAC: 2208-001 EMAC 2208-001
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https://emac.gsfc.nasa.gov?cid=2208-001
ALMA: A Fortran program for computing the viscoelastic Love numbers of a spherically symmetric planet

Melini, D., Saliby, C., Spada, G.

ALMA is a Fortran code that computes loading and tidal Love numbers for a spherically symmetric, radially stratified, incompressible planet. ALMA can evaluate i) real (time-domain) Love numbers and their time derivatives for a Heaviside or ramp-shaped forcing time history, or ii) complex (frequency-domain) Love numbers for a periodic forcing. The planetary structure can include an arbitrary number of homogeneous layers, and each layer can have a different rheological law. ALMA can model the following linear rheologies: Elastic, Maxwell visco-elastic, Newtonian viscous fluid, Kelvin-Voigt solid, Burgers and Andrade transient rheologies. Additional rheological laws can be easily implemented.

Last updated: Aug. 16, 2022

Code Language(s): Fortran

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https://emac.gsfc.nasa.gov?cid=2208-001
2208-001

ALMA: A Fortran program for computing the viscoelastic Love numbers of a spherically symmetric planet

Melini, D., Saliby, C., Spada, G.

ALMA is a Fortran code that computes loading and tidal Love numbers for a spherically symmetric, radially stratified, incompressible planet. ALMA can evaluate i) real (time-domain) Love numbers and their time derivatives for a Heaviside or ramp-shaped forcing time history, or ii) complex (frequency-domain) Love numbers for a periodic forcing. The planetary structure can include an arbitrary number of homogeneous layers, and each layer can have a different rheological law. ALMA can model the following linear rheologies: Elastic, Maxwell visco-elastic, Newtonian viscous fluid, Kelvin-Voigt solid, Burgers and Andrade transient rheologies. Additional rheological laws can be easily implemented.

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EMAC: 2207-138 EMAC 2207-138
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https://emac.gsfc.nasa.gov?cid=2207-138
VPLanet: The Virtual Planet Simulator

Rory Barnes et al.

VPLanet simulates planetary system evolution with a single executable: 1) thermal and magnetic evolution of terrestrial planets, 2) magma oceans, 3) radiogenic heating of interiors, 4) tidal effects, 5) rotational axis evolution, 6) stellar evolution, including pre-MS, XUV, and spin-down, 7) stellar flares, 8) climate via a 1-D EBM, 9) atmospheric escape, including water photolysis and H escape, 10) approximate orbital evolution, 11) exact orbital evolution, 12) circumbinary planet orbits, and 13) galactic perturbations on planetary systems. The code is validated by reproducing selected Solar System, exoplanet, and binary star properties. Documentation and numerous examples are provided.

Last updated: Aug. 10, 2022

Code Language(s): C, Python3

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https://emac.gsfc.nasa.gov?cid=2207-138
2207-138

VPLanet: The Virtual Planet Simulator

Rory Barnes et al.

VPLanet simulates planetary system evolution with a single executable: 1) thermal and magnetic evolution of terrestrial planets, 2) magma oceans, 3) radiogenic heating of interiors, 4) tidal effects, 5) rotational axis evolution, 6) stellar evolution, including pre-MS, XUV, and spin-down, 7) stellar flares, 8) climate via a 1-D EBM, 9) atmospheric escape, including water photolysis and H escape, 10) approximate orbital evolution, 11) exact orbital evolution, 12) circumbinary planet orbits, and 13) galactic perturbations on planetary systems. The code is validated by reproducing selected Solar System, exoplanet, and binary star properties. Documentation and numerous examples are provided.

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EMAC: 2207-176 EMAC 2207-176
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https://emac.gsfc.nasa.gov?cid=2207-176
Eureka!: An End-to-End Pipeline for JWST Time-Series Observations

Bell, T. J. et al.

Eureka! is a data reduction and analysis pipeline for exoplanet time-series observations, with a particular focus on James Webb Space Telescope (JWST) data. The goal of Eureka! is to provide an end-to-end pipeline that starts with raw, uncalibrated FITS files and ultimately yields precise exoplanet transmission and/or emission spectra. The pipeline has a modular structure with six stages, each with intermediate figures and outputs that allow users to compare Eureka!’s performance using different parameter settings or to compare Eureka! with an independent pipeline.

Last updated: Jul. 20, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-176
2207-176

Eureka!: An End-to-End Pipeline for JWST Time-Series Observations

Bell, T. J. et al.

Eureka! is a data reduction and analysis pipeline for exoplanet time-series observations, with a particular focus on James Webb Space Telescope (JWST) data. The goal of Eureka! is to provide an end-to-end pipeline that starts with raw, uncalibrated FITS files and ultimately yields precise exoplanet transmission and/or emission spectra. The pipeline has a modular structure with six stages, each with intermediate figures and outputs that allow users to compare Eureka!’s performance using different parameter settings or to compare Eureka! with an independent pipeline.

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EMAC: 2207-001 EMAC 2207-001
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https://emac.gsfc.nasa.gov?cid=2207-001
Pytmosph3R: Transmission/emission spectra from 3D atmospheric simulations (GCMs, ...)

Falco, Aurélien ; Pluriel, William ; Leconte, Jérémy ; Caldas, Anthony

Pytmosph3R is a Python-3 library that computes transmission and emission spectra based on 3D atmospheric simulations, for example performed with the LMDZ generic global climate model. Pytmosph3R can be used in notebooks or on the command line, using a configuration similar to that of TauREx. The library should include a feature to generate phase/light-curves in the next release.

Last updated: Jun. 28, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-001
2207-001

Pytmosph3R: Transmission/emission spectra from 3D atmospheric simulations (GCMs, ...)

Falco, Aurélien ; Pluriel, William ; Leconte, Jérémy ; Caldas, Anthony

Pytmosph3R is a Python-3 library that computes transmission and emission spectra based on 3D atmospheric simulations, for example performed with the LMDZ generic global climate model. Pytmosph3R can be used in notebooks or on the command line, using a configuration similar to that of TauREx. The library should include a feature to generate phase/light-curves in the next release.

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EMAC: 2207-002 EMAC 2207-002
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celmech: A Python package for celestial mechanics

Hadden, Sam; Tamayo, Daniel

An open-source Python package designed to facilitate a wide variety of celestial mechanics calculations. The package allows users to formulate and integrate equations of motion by incorporating user-specified terms from the classical disturbing function expansion. The package is designed to interface seamlessly with the popular REBOUND N-body code.

Last updated: Jun. 14, 2022

Code Language(s): Python3, C

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https://emac.gsfc.nasa.gov?cid=2207-002
2207-002

celmech: A Python package for celestial mechanics

Hadden, Sam; Tamayo, Daniel

An open-source Python package designed to facilitate a wide variety of celestial mechanics calculations. The package allows users to formulate and integrate equations of motion by incorporating user-specified terms from the classical disturbing function expansion. The package is designed to interface seamlessly with the popular REBOUND N-body code.

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EMAC: 2207-003 EMAC 2207-003
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https://emac.gsfc.nasa.gov?cid=2207-003
pycheops: Python package for the analysis of light curves from the ESA CHEOPS mission

Maxted et al.

The pycheops python module is an open-source software package that has been developed to easily and efficiently analyse CHEOPS light curve data using state-of-the-art techniques. The models in the package can also be applied to other types of data. The package included a "cook book" and examples, plus a command-line tool that aids in the preparation of observing requests for CHEOPS observers (make_xml_files). For discussion and announcements, please join the pycheops google group.

Last updated: Jun. 14, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-003
2207-003

pycheops: Python package for the analysis of light curves from the ESA CHEOPS mission

Maxted et al.

The pycheops python module is an open-source software package that has been developed to easily and efficiently analyse CHEOPS light curve data using state-of-the-art techniques. The models in the package can also be applied to other types of data. The package included a "cook book" and examples, plus a command-line tool that aids in the preparation of observing requests for CHEOPS observers (make_xml_files). For discussion and announcements, please join the pycheops google group.

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EMAC: 2207-004 EMAC 2207-004
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https://emac.gsfc.nasa.gov?cid=2207-004
HELIOS-K: A GPU opacity calculator for exoplanetary atmospheres

Simon Grimm, Kevin Heng

HELIOS-K calculates opacity functions for planetary atmopheres by using opacity line lists from different databases. Before the opacity functions can be calculated, the line lists need to be downloaded and preprocessed into binary files that can be read from HELIOS-K. HELIOS-K provides tools to automatically download and preprocess files from the ExoMol, HITRAN, HITEMP, NIST, Kurucz and VALD3 databases. HELIOS-K is running on GPUs and require a Nvidia GPU with compute capability of 3.0 or higher.

Last updated: May. 31, 2022

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

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https://emac.gsfc.nasa.gov?cid=2207-004
2207-004

HELIOS-K: A GPU opacity calculator for exoplanetary atmospheres

Simon Grimm, Kevin Heng

HELIOS-K calculates opacity functions for planetary atmopheres by using opacity line lists from different databases. Before the opacity functions can be calculated, the line lists need to be downloaded and preprocessed into binary files that can be read from HELIOS-K. HELIOS-K provides tools to automatically download and preprocess files from the ExoMol, HITRAN, HITEMP, NIST, Kurucz and VALD3 databases. HELIOS-K is running on GPUs and require a Nvidia GPU with compute capability of 3.0 or higher.

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EMAC: 2207-005 EMAC 2207-005
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https://emac.gsfc.nasa.gov?cid=2207-005
l1periodogram: Searching for planetary signatures in radial velocity time-series with a sparse recovery technique

Nathan C. Hara

The l1 periodogram is designed to search for periodicities in unevenly sampled time series. It can be used similarly as a Lomb-Scargle periodogram, and retrieves a figure which has a similar aspect but has fewer peaks due to aliasing. It is primarily designed for the search of exoplanets in radial velocity data, but can be also used for other purposes. It is based the sparse recovery technique called "Basis Pursuit" (Chen & Donoho 1998).

Last updated: May. 24, 2022

Code Language(s): Python3, Fortran

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https://emac.gsfc.nasa.gov?cid=2207-005
2207-005

l1periodogram: Searching for planetary signatures in radial velocity time-series with a sparse recovery technique

Nathan C. Hara

The l1 periodogram is designed to search for periodicities in unevenly sampled time series. It can be used similarly as a Lomb-Scargle periodogram, and retrieves a figure which has a similar aspect but has fewer peaks due to aliasing. It is primarily designed for the search of exoplanets in radial velocity data, but can be also used for other purposes. It is based the sparse recovery technique called "Basis Pursuit" (Chen & Donoho 1998).

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EMAC: 2207-006 EMAC 2207-006
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https://emac.gsfc.nasa.gov?cid=2207-006
Magnitude-squared coherence: Frequency-domain view of the cross-correlation between RV and activity indicator time series

Dodson-Robinson, S. E., et al.

NWelch is a python3 implementation of Welch's method for estimating the magnitude-squared coherence (MSC) between contemporaneous RV and activity-indicator time series. While it's impossible to directly calculate the cross-correlation between two unevenly sampled time series, we can use a nonuniform fast Fourier transform to estimate the frequency-domain version of their cross-correlation - the MSC. Observers should be suspicious of planet candidates at frequencies with high MSC. For univariate time series (for example, RV only), Welch's method can deliver power spectrum estimates with lower variance than the Lomb-Scargle periodogram.

Last updated: May. 24, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-006
2207-006

Magnitude-squared coherence: Frequency-domain view of the cross-correlation between RV and activity indicator time series

Dodson-Robinson, S. E., et al.

NWelch is a python3 implementation of Welch's method for estimating the magnitude-squared coherence (MSC) between contemporaneous RV and activity-indicator time series. While it's impossible to directly calculate the cross-correlation between two unevenly sampled time series, we can use a nonuniform fast Fourier transform to estimate the frequency-domain version of their cross-correlation - the MSC. Observers should be suspicious of planet candidates at frequencies with high MSC. For univariate time series (for example, RV only), Welch's method can deliver power spectrum estimates with lower variance than the Lomb-Scargle periodogram.

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EMAC: 2207-007 EMAC 2207-007
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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.

Last updated: May. 10, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-007
2207-007

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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EMAC: 2207-008 EMAC 2207-008
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https://emac.gsfc.nasa.gov?cid=2207-008
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.

Last updated: May. 10, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-008
2207-008

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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EMAC: 2207-009 EMAC 2207-009
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https://emac.gsfc.nasa.gov?cid=2207-009
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.

Last updated: May. 3, 2022

Code Language(s): java, javascript

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https://emac.gsfc.nasa.gov?cid=2207-009
2207-009

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.

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EMAC: 2207-010 EMAC 2207-010
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https://emac.gsfc.nasa.gov?cid=2207-010
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).

Last updated: May. 3, 2022

Code Language(s): Python3, javascript

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https://emac.gsfc.nasa.gov?cid=2207-010
2207-010

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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EMAC: 2207-011 EMAC 2207-011
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https://emac.gsfc.nasa.gov?cid=2207-011
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.

Last updated: Apr. 26, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-011
2207-011

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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EMAC: 2207-012 EMAC 2207-012
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https://emac.gsfc.nasa.gov?cid=2207-012
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.

Last updated: Apr. 19, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-012
2207-012

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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EMAC: 2207-013 EMAC 2207-013
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https://emac.gsfc.nasa.gov?cid=2207-013
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

Last updated: Mar. 29, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-013
2207-013

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

EMAC: 2207-014 EMAC 2207-014
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https://emac.gsfc.nasa.gov?cid=2207-014
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.

Last updated: Mar. 15, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-014
2207-014

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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EMAC: 2207-015 EMAC 2207-015
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https://emac.gsfc.nasa.gov?cid=2207-015
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.

Last updated: Mar. 15, 2022

Code Language(s): CUDA, C

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https://emac.gsfc.nasa.gov?cid=2207-015
2207-015

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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EMAC: 2207-016 EMAC 2207-016
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https://emac.gsfc.nasa.gov?cid=2207-016
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.

Last updated: Mar. 1, 2022

Code Language(s): C, Python2

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https://emac.gsfc.nasa.gov?cid=2207-016
2207-016

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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EMAC: 2207-017 EMAC 2207-017
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https://emac.gsfc.nasa.gov?cid=2207-017
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.

Last updated: Feb. 22, 2022

Code Language(s): Julia

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https://emac.gsfc.nasa.gov?cid=2207-017
2207-017

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.

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EMAC: 2207-018 EMAC 2207-018
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https://emac.gsfc.nasa.gov?cid=2207-018
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.

Last updated: Feb. 22, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-018
2207-018

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.

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EMAC: 2207-019 EMAC 2207-019
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https://emac.gsfc.nasa.gov?cid=2207-019
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.

Last updated: Feb. 22, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-019
2207-019

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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EMAC: 2207-020 EMAC 2207-020
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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.

Last updated: Feb. 22, 2022

Code Language(s): Matlab

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https://emac.gsfc.nasa.gov?cid=2207-020
2207-020

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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EMAC: 2207-021 EMAC 2207-021
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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.

Last updated: Feb. 15, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-021
2207-021

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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EMAC: 2207-022 EMAC 2207-022
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https://emac.gsfc.nasa.gov?cid=2207-022
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.

Last updated: Feb. 15, 2022

Code Language(s): Fortran

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2207-022

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.

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EMAC: 2207-023 EMAC 2207-023
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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.

Last updated: Feb. 15, 2022

Code Language(s): Python3, Fortran

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2207-023

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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EMAC: 2207-024 EMAC 2207-024
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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.

Last updated: Feb. 8, 2022

Code Language(s): Python3

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2207-024

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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EMAC: 2207-025 EMAC 2207-025
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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/).

Last updated: Feb. 8, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-025
2207-025

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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EMAC: 2207-026 EMAC 2207-026
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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.

Last updated: Feb. 8, 2022

Code Language(s): Fortran, Python3

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2207-026

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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EMAC: 2207-027 EMAC 2207-027
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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.

Last updated: Feb. 8, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-027
2207-027

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.

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EMAC: 2207-028 EMAC 2207-028
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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.

Last updated: Feb. 8, 2022

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-028
2207-028

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.

Demo
EMAC: 2207-029 EMAC 2207-029
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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.

Last updated: Feb. 4, 2022

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

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https://emac.gsfc.nasa.gov?cid=2207-029
2207-029

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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EMAC: 2207-030 EMAC 2207-030
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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.

Last updated: Jan. 18, 2022

Code Language(s): C++, Python3

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https://emac.gsfc.nasa.gov?cid=2207-030
2207-030

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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EMAC: 2207-031 EMAC 2207-031
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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).

Last updated: Dec. 14, 2021

Code Language(s): Python3

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2207-031

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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EMAC: 2207-032 EMAC 2207-032
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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.

Last updated: Dec. 14, 2021

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-032
2207-032

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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EMAC: 2207-033 EMAC 2207-033
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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.

Last updated: Dec. 14, 2021

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-033
2207-033

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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EMAC: 2207-034 EMAC 2207-034
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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.

Last updated: Dec. 10, 2021

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-034
2207-034

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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EMAC: 2207-035 EMAC 2207-035
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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.

Last updated: Dec. 7, 2021

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-035
2207-035

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.

EMAC: 2207-036 EMAC 2207-036
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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.

Last updated: Nov. 30, 2021

Code Language(s): Python3, C++

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https://emac.gsfc.nasa.gov?cid=2207-036
2207-036

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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EMAC: 2207-037 EMAC 2207-037
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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.

Last updated: Nov. 23, 2021

Code Language(s): CUDA Fortran

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https://emac.gsfc.nasa.gov?cid=2207-037
2207-037

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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EMAC: 2207-038 EMAC 2207-038
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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.

Last updated: Nov. 16, 2021

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-038
2207-038

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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EMAC: 2207-039 EMAC 2207-039
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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.

Last updated: Nov. 16, 2021

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-039
2207-039

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