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.

TRES-exo: TRiple Evolution Simulation package

Columba, G.; Toonen, S.; Dorozsmai, A.; Danielski, C.

EMAC: 2402-001 EMAC 2402-001
copy_img
https://emac.gsfc.nasa.gov?cid=2402-001

TRES is a numerical framework for simulating hierarchical triple systems with stellar and planetary components. It accounts for three-body dynamics, stellar evolution and various interactions. TRES-exo is an extension of the original code (Toonen et al. 2016) specifically designed to simulate circumbinary planets and their evolution.

Last updated: Feb. 6, 2024

Code Language(s): Python, C

TRES-exo: TRiple Evolution Simulation package

Columba, G.; Toonen, S.; Dorozsmai, A.; Danielski, C.

copy_img
https://emac.gsfc.nasa.gov?cid=2402-001
2402-001

TRES is a numerical framework for simulating hierarchical triple systems with stellar and planetary components. It accounts for three-body dynamics, stellar evolution and various interactions. TRES-exo is an extension of the original code (Toonen et al. 2016) specifically designed to simulate circumbinary planets and their evolution.

About
Spright: Bayesian mass-radius relation for small planets

Parviainen, H.; Luque, R.; Palle, E.

EMAC: 2401-006 EMAC 2401-006
copy_img
https://emac.gsfc.nasa.gov?cid=2401-006

Spright is a fast Bayesian radius-density-mass relation for small planets. The package allows one to predict planetary masses, densities, and RV semi-amplitudes given the planet's radius or planetary radii given the planet's mass. The package offers an easy-to-use command line script for people not overly interested in coding and a nearly-as-easy-to-use set of Python classes for those who prefer to code. The command line script can directly create publication-quality plots, and the classes offer a full access to the predicted numerical distributions.

Last updated: Jan. 19, 2024

Code Language(s): Python3

Spright: Bayesian mass-radius relation for small planets

Parviainen, H.; Luque, R.; Palle, E.

copy_img
https://emac.gsfc.nasa.gov?cid=2401-006
2401-006

Spright is a fast Bayesian radius-density-mass relation for small planets. The package allows one to predict planetary masses, densities, and RV semi-amplitudes given the planet's radius or planetary radii given the planet's mass. The package offers an easy-to-use command line script for people not overly interested in coding and a nearly-as-easy-to-use set of Python classes for those who prefer to code. The command line script can directly create publication-quality plots, and the classes offer a full access to the predicted numerical distributions.

About
VCAL-SPHERE: Hybrid pipeline for reduction of VLT/SPHERE data

Christiaens, Valentin et al.

EMAC: 2401-005 EMAC 2401-005
copy_img
https://emac.gsfc.nasa.gov?cid=2401-005

VCAL-SPHERE, for VIP-based Calibration of VLT/SPHERE data, is a versatile pipeline for high-contrast imaging of exoplanets and circumstellar disks. The pipeline covers all steps of data reduction, including raw calibration, pre-processing and post-processing (i.e., modeling and subtraction of the stellar halo), for the IFS, IRDIS-DBI and IRDIS-CI modes (and combinations thereof) of the VLT instrument SPHERE. The three main steps of the reduction correspond to different modules, where the first follows the recommended EsoRex (ascl:1504.003) workflow and associated recipes with occasional inclusion of VIP (ascl:1603.003) routines.

Last updated: Jan. 16, 2024

Code Language(s): Python3

VCAL-SPHERE: Hybrid pipeline for reduction of VLT/SPHERE data

Christiaens, Valentin et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2401-005
2401-005

VCAL-SPHERE, for VIP-based Calibration of VLT/SPHERE data, is a versatile pipeline for high-contrast imaging of exoplanets and circumstellar disks. The pipeline covers all steps of data reduction, including raw calibration, pre-processing and post-processing (i.e., modeling and subtraction of the stellar halo), for the IFS, IRDIS-DBI and IRDIS-CI modes (and combinations thereof) of the VLT instrument SPHERE. The three main steps of the reduction correspond to different modules, where the first follows the recommended EsoRex (ascl:1504.003) workflow and associated recipes with occasional inclusion of VIP (ascl:1603.003) routines.

About
deconfuser: An algorithm for fast orbit-fitting of directly imaged planets

Pogorelyuk, L. et al.

EMAC: 2401-004 EMAC 2401-004
copy_img
https://emac.gsfc.nasa.gov?cid=2401-004

Fast orbit fitting of directly imaged multi-planetary systems. The deconfuser quickly fits orbits to planet detections in 2D images, guarantees that all orbits within a certain tolerance are found, and ranks partitions of detections by planets (decides which assignment of detection-to-planet fits the data best). Pogorelyuk et al. 2022 describes the deconfusion algorithm and estimates of confusion rates from simulated planetary systems using the deconfuser.

Last updated: Jan. 5, 2024

Code Language(s): Python3

deconfuser: An algorithm for fast orbit-fitting of directly imaged planets

Pogorelyuk, L. et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2401-004
2401-004

Fast orbit fitting of directly imaged multi-planetary systems. The deconfuser quickly fits orbits to planet detections in 2D images, guarantees that all orbits within a certain tolerance are found, and ranks partitions of detections by planets (decides which assignment of detection-to-planet fits the data best). Pogorelyuk et al. 2022 describes the deconfusion algorithm and estimates of confusion rates from simulated planetary systems using the deconfuser.

About Demo
AESTRA: Auto-Encoding STellar Radial-velocity and Activity

Liang, Yan et al.

EMAC: 2401-003 EMAC 2401-003
copy_img
https://emac.gsfc.nasa.gov?cid=2401-003

AESTRA (Auto-Encoding STellar Radial-velocity and Activity) is a deep learning method for precise radial velocity measurements in the presence of stellar activity noise. The architecture combines a convolutional radial-velocity estimator and a spectrum auto-encoder called spender. For an in-depth understanding of the spectrum auto-encoder, see Melchior et al. 2023 and Liang et al. 2023.

Last updated: Jan. 2, 2024

Code Language(s): Python3

AESTRA: Auto-Encoding STellar Radial-velocity and Activity

Liang, Yan et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2401-003
2401-003

AESTRA (Auto-Encoding STellar Radial-velocity and Activity) is a deep learning method for precise radial velocity measurements in the presence of stellar activity noise. The architecture combines a convolutional radial-velocity estimator and a spectrum auto-encoder called spender. For an in-depth understanding of the spectrum auto-encoder, see Melchior et al. 2023 and Liang et al. 2023.

About
sai: Surface-Atmosphere Interactions on Warm Exoplanets

Byrne, Xander et al.

EMAC: 2401-002 EMAC 2401-002
copy_img
https://emac.gsfc.nasa.gov?cid=2401-002

For warm rocky planets, broadly Venus-like planets, the high temperatures and moderate pressures at the base of their atmospheres may enable thermochemical equilibrium between rock and gas. This links the composition of the surface to that of the observable atmosphere. sai is a repository containing files for the GGchem equilibrium chemistry code, and associated helper functions, which we used to find a boundary in surface pressure-temperature space which simultaneously separates distinct mineralogical regimes and atmospheric regimes, potentially enabling inference of surface mineralogy from spectroscopic observations of the atmosphere (Byrne+23, MNRAS).

Last updated: Jan. 2, 2024

Code Language(s): Python3

sai: Surface-Atmosphere Interactions on Warm Exoplanets

Byrne, Xander et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2401-002
2401-002

For warm rocky planets, broadly Venus-like planets, the high temperatures and moderate pressures at the base of their atmospheres may enable thermochemical equilibrium between rock and gas. This links the composition of the surface to that of the observable atmosphere. sai is a repository containing files for the GGchem equilibrium chemistry code, and associated helper functions, which we used to find a boundary in surface pressure-temperature space which simultaneously separates distinct mineralogical regimes and atmospheric regimes, potentially enabling inference of surface mineralogy from spectroscopic observations of the atmosphere (Byrne+23, MNRAS).

About
VSPEC: Variable Star PhasE Curve

Johnson, Ted; Kelahan, Cameron et al.

EMAC: 2401-001 EMAC 2401-001
copy_img
https://emac.gsfc.nasa.gov?cid=2401-001

VSPEC (Variable Star PhasE Curve) is an exoplanet modeling suite that combines NASA’s Planetary Spectrum Generator (PSG) with a custom variable star. Originally built to simulate the infrared excess of non-transiting planets, the code supports transit, eclipse, phase curve geometries as well as spots, faculae, flares, granulation, and the transit light source effect. Install it with pip or see the documentation linked below.

Last updated: Jan. 2, 2024

Code Language(s): Python3

VSPEC: Variable Star PhasE Curve

Johnson, Ted; Kelahan, Cameron et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2401-001
2401-001

VSPEC (Variable Star PhasE Curve) is an exoplanet modeling suite that combines NASA’s Planetary Spectrum Generator (PSG) with a custom variable star. Originally built to simulate the infrared excess of non-transiting planets, the code supports transit, eclipse, phase curve geometries as well as spots, faculae, flares, granulation, and the transit light source effect. Install it with pip or see the documentation linked below.

About
pycrires: Data reduction pipeline for VLT/CRIRES+

Stolker, Tomas; Landman, Rico

EMAC: 2312-001 EMAC 2312-001
copy_img
https://emac.gsfc.nasa.gov?cid=2312-001

pycrires runs the CRIRES+ recipes of EsoRex. The pipeline organizes the raw data, creates SOF and configuration files, runs the calibration and science recipes, and creates plots of the images and extracted spectra. Additionally, it corrects remaining inaccuracies in the wavelength solution and the spectrum curvature. pycrires also provides dedicated routines for the extraction, calibration, and detection of spatially-resolved objects such as directly imaged planets.

Last updated: Dec. 27, 2023

Code Language(s): Python3

pycrires: Data reduction pipeline for VLT/CRIRES+

Stolker, Tomas; Landman, Rico

copy_img
https://emac.gsfc.nasa.gov?cid=2312-001
2312-001

pycrires runs the CRIRES+ recipes of EsoRex. The pipeline organizes the raw data, creates SOF and configuration files, runs the calibration and science recipes, and creates plots of the images and extracted spectra. Additionally, it corrects remaining inaccuracies in the wavelength solution and the spectrum curvature. pycrires also provides dedicated routines for the extraction, calibration, and detection of spatially-resolved objects such as directly imaged planets.

About
ExPRES: Exoplanetary and Planetary Radio Emission Simulator

Louis, C. K. et al.

EMAC: 2311-007 EMAC 2311-007
copy_img
https://emac.gsfc.nasa.gov?cid=2311-007

ExPRES (Exoplanetary and Planetary Radio Emission Simulator) is a versatile tool that computes the observation opportunities of planetary radio emissions, based on the radio source beaming patterns and the observer’s location. The ExPRES code is assuming that auroral radio waves are emitted through the Cyclotron Maser Instability (CMI). This emission mechanism can transfer free energy present in the electron distribution function in the source, into the ambient electromagnetic fluctuation background, thus amplifying waves at a frequency close to the local electron cyclotron frequency, as a resonator.

Last updated: Nov. 27, 2023

Code Language(s): IDL, Python3

ExPRES: Exoplanetary and Planetary Radio Emission Simulator

Louis, C. K. et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2311-007
2311-007

ExPRES (Exoplanetary and Planetary Radio Emission Simulator) is a versatile tool that computes the observation opportunities of planetary radio emissions, based on the radio source beaming patterns and the observer’s location. The ExPRES code is assuming that auroral radio waves are emitted through the Cyclotron Maser Instability (CMI). This emission mechanism can transfer free energy present in the electron distribution function in the source, into the ambient electromagnetic fluctuation background, thus amplifying waves at a frequency close to the local electron cyclotron frequency, as a resonator.

About
MAGPy-RV: Modelling stellar Activity with Gaussian Processes in Radial Velocity

Rescigno, Federica ; Dixon, Bryce ; Haywood, Raphaëlle D.

EMAC: 2311-006 EMAC 2311-006
copy_img
https://emac.gsfc.nasa.gov?cid=2311-006

MAGPy-RV models data with Gaussian Process regression and affine invariant Monte Carlo Markov Chain parameter searching. Developed to model intrinsic, quasi-periodic variations induced by the host star in radial velocity (RV) surveys for the detection of exoplanets and the accurate measurements of their orbital parameters and masses, it now includes a variety of kernels and models and can be applied to any time-series analysis. MAGPy-RV includes publication level plotting, efficient posterior extraction, and export-ready LaTeX results tables. It also handles multiple datasets at once and can model offsets and systematics from multiple instruments.

Last updated: Nov. 27, 2023

Code Language(s): Python3

MAGPy-RV: Modelling stellar Activity with Gaussian Processes in Radial Velocity

Rescigno, Federica ; Dixon, Bryce ; Haywood, Raphaëlle D.

copy_img
https://emac.gsfc.nasa.gov?cid=2311-006
2311-006

MAGPy-RV models data with Gaussian Process regression and affine invariant Monte Carlo Markov Chain parameter searching. Developed to model intrinsic, quasi-periodic variations induced by the host star in radial velocity (RV) surveys for the detection of exoplanets and the accurate measurements of their orbital parameters and masses, it now includes a variety of kernels and models and can be applied to any time-series analysis. MAGPy-RV includes publication level plotting, efficient posterior extraction, and export-ready LaTeX results tables. It also handles multiple datasets at once and can model offsets and systematics from multiple instruments.

About
NcorpiON: An O(N) software for N-body integration in collisional and fragmenting systems

Couturier, J.; Quillen, A. C.; Nakajima, M.

EMAC: 2311-005 EMAC 2311-005
copy_img
https://emac.gsfc.nasa.gov?cid=2311-005

NcorpiON is an N-body software developed for the time-efficient integration of collisional and fragmenting systems of planetesimals or moonlets orbiting a central mass. It features a fragmentation model, based on crater scaling and ejecta models, able to realistically simulate a violent impact. NcorpiON is designed for the study of accreting or fragmenting disks of planetesimal or moonlets. It detects collisions and computes mutual gravity faster than REBOUND, and unlike other N-body integrators, it can resolve a collision by fragmentation. The fast multipole expansions are implemented up to order six to allow for a high precision in mutual gravity computation.

Last updated: Nov. 27, 2023

Code Language(s): C

NcorpiON: An O(N) software for N-body integration in collisional and fragmenting systems

Couturier, J.; Quillen, A. C.; Nakajima, M.

copy_img
https://emac.gsfc.nasa.gov?cid=2311-005
2311-005

NcorpiON is an N-body software developed for the time-efficient integration of collisional and fragmenting systems of planetesimals or moonlets orbiting a central mass. It features a fragmentation model, based on crater scaling and ejecta models, able to realistically simulate a violent impact. NcorpiON is designed for the study of accreting or fragmenting disks of planetesimal or moonlets. It detects collisions and computes mutual gravity faster than REBOUND, and unlike other N-body integrators, it can resolve a collision by fragmentation. The fast multipole expansions are implemented up to order six to allow for a high precision in mutual gravity computation.

About
PBjam

Carboneau, Lindsey; Davies, Guy; Hall, Oliver; Lyttle, Alex; Nielsen, Martin

EMAC: 2311-004 EMAC 2311-004
copy_img
https://emac.gsfc.nasa.gov?cid=2311-004

PBjam is toolbox for modeling the oscillation spectra of solar-like oscillators. This involves two main parts: identifying a set of modes of interest, and accurately modeling those modes to measure their frequencies. Currently, the mode identification is based on fitting the asymptotic relation to the l=2,0 pairs, relying on the cumulative sum of prior knowledge gained from NASA's Kepler mission to inform the fitting process. Modeling the modes, or 'peakbagging', is done using the HMC sampler from pymc3, which fits a Lorentzian to each of the identified modes, with much fewer priors than during he mode ID process.

Last updated: Nov. 17, 2023

Code Language(s): Python3

PBjam

Carboneau, Lindsey; Davies, Guy; Hall, Oliver; Lyttle, Alex; Nielsen, Martin

copy_img
https://emac.gsfc.nasa.gov?cid=2311-004
2311-004

PBjam is toolbox for modeling the oscillation spectra of solar-like oscillators. This involves two main parts: identifying a set of modes of interest, and accurately modeling those modes to measure their frequencies. Currently, the mode identification is based on fitting the asymptotic relation to the l=2,0 pairs, relying on the cumulative sum of prior knowledge gained from NASA's Kepler mission to inform the fitting process. Modeling the modes, or 'peakbagging', is done using the HMC sampler from pymc3, which fits a Lorentzian to each of the identified modes, with much fewer priors than during he mode ID process.

About
DACE: Data & Analysis Center for Exoplanets

Ségransan, D. et al.

EMAC: 2311-003 EMAC 2311-003
copy_img
https://emac.gsfc.nasa.gov?cid=2311-003

The Data & Analysis Center for Exoplanets (DACE) is a PlanetS web-platform located at the University of Geneva (CH) dedicated to extrasolar planets data visualisation, exchange and analysis. DACE provides the research and education community with an enhanced access to exoplanet data with a suite of statistical tools for data analysis. Published observational data such as high resolution spectra, radial velocities, photometric light curves and high contrast imaging measurements are available online. Planetary systems formation and evolution can be studied as well as their long term dynamical evolution.

Last updated: Nov. 9, 2023

Code Language(s): N/A

DACE: Data & Analysis Center for Exoplanets

Ségransan, D. et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2311-003
2311-003

The Data & Analysis Center for Exoplanets (DACE) is a PlanetS web-platform located at the University of Geneva (CH) dedicated to extrasolar planets data visualisation, exchange and analysis. DACE provides the research and education community with an enhanced access to exoplanet data with a suite of statistical tools for data analysis. Published observational data such as high resolution spectra, radial velocities, photometric light curves and high contrast imaging measurements are available online. Planetary systems formation and evolution can be studied as well as their long term dynamical evolution.

Demo
tapify: A Multitaper Periodogram package for computing the power spectrum of a time-series with minimal spec

Patil, A. et al.

EMAC: 2311-002 EMAC 2311-002
copy_img
https://emac.gsfc.nasa.gov?cid=2311-002

tapify is a Python package that implements a suite of multitaper spectral estimation techniques for analyzing time series data. It supports analysis of both evenly and unevenly sampled time series data. The multitaper statistic was first proposed by Thomson (1982) as a non-parametric estimator of the spectrum of a time series. It is attractive because it tackles the problems of bias and consistency, which makes it an improvement over the classical periodogram for evenly sampled data and the Lomb-Scargle periodogram for uneven sampling. In basic statistical terms, this estimator allows us to confidently look at the properties of a time series in the frequency or Fourier domain.

Last updated: Nov. 9, 2023

Code Language(s): Python3

tapify: A Multitaper Periodogram package for computing the power spectrum of a time-series with minimal spec

Patil, A. et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2311-002
2311-002

tapify is a Python package that implements a suite of multitaper spectral estimation techniques for analyzing time series data. It supports analysis of both evenly and unevenly sampled time series data. The multitaper statistic was first proposed by Thomson (1982) as a non-parametric estimator of the spectrum of a time series. It is attractive because it tackles the problems of bias and consistency, which makes it an improvement over the classical periodogram for evenly sampled data and the Lomb-Scargle periodogram for uneven sampling. In basic statistical terms, this estimator allows us to confidently look at the properties of a time series in the frequency or Fourier domain.

About
MADYS: Isochronal parameter determination for young stellar and substellar objects

Squicciarini, V., & Bonavita, M.

EMAC: 2311-001 EMAC 2311-001
copy_img
https://emac.gsfc.nasa.gov?cid=2311-001

MADYS (Manifold Age Determination for Young Stars) determines astrophysical parameters (such as age, mass, radius and Teff) of young stellar and substellar objects. The code automatically retrieves and cross-matches photometry from several catalogs, estimates interstellar extinction, and derives parameter estimates for individual objects through isochronal fitting. Harmonizing the heterogeneity of publicly-available isochrone grids, MADYS enables its users to choose amongst >140 grids from >20 models. Its versatility allows for a wide range of scientific applications, ranging from the characterization of directly imaged planets to the study of stellar associations.

Last updated: Nov. 9, 2023

Code Language(s): Python3

MADYS: Isochronal parameter determination for young stellar and substellar objects

Squicciarini, V., & Bonavita, M.

copy_img
https://emac.gsfc.nasa.gov?cid=2311-001
2311-001

MADYS (Manifold Age Determination for Young Stars) determines astrophysical parameters (such as age, mass, radius and Teff) of young stellar and substellar objects. The code automatically retrieves and cross-matches photometry from several catalogs, estimates interstellar extinction, and derives parameter estimates for individual objects through isochronal fitting. Harmonizing the heterogeneity of publicly-available isochrone grids, MADYS enables its users to choose amongst >140 grids from >20 models. Its versatility allows for a wide range of scientific applications, ranging from the characterization of directly imaged planets to the study of stellar associations.

About
ExoFOP: The Exoplanet Follow-up Observing Program

NASA Exoplanet Archive team at NExScI/IPAC

EMAC: 2310-007 EMAC 2310-007
copy_img
https://emac.gsfc.nasa.gov?cid=2310-007

This website is designed to optimize resources and facilitate collaboration in follow-up studies of exoplanet candidates. ExoFOP serves as a repository for project and community-gathered data by allowing upload and display of data and derived astrophysical parameters.

Last updated: Oct. 26, 2023

Code Language(s): N/A

ExoFOP: The Exoplanet Follow-up Observing Program

NASA Exoplanet Archive team at NExScI/IPAC

copy_img
https://emac.gsfc.nasa.gov?cid=2310-007
2310-007

This website is designed to optimize resources and facilitate collaboration in follow-up studies of exoplanet candidates. ExoFOP serves as a repository for project and community-gathered data by allowing upload and display of data and derived astrophysical parameters.

About Demo
pycdata: A module to import datasets from various instruments in pycheops

Jayshil A. Patel, Alexis Brandeker, Pierre Maxted

EMAC: 2310-006 EMAC 2310-006
copy_img
https://emac.gsfc.nasa.gov?cid=2310-006

pycdata is a module to import datasets from various telescopes/instruments in pycheops. pycheops is a tool specifically designed to model CHEOPS observations of transits, eclipses and phase curves. While being a genius tool, what it lacks is a facility to model datasets from other telescopes/instruments, even the PSF photometry produced by PIPE. pycdata can be used to import datasets from PIPE, TESS and Kepler/K2 in pycheops thus enabling a joint lightcurve analysis of PIPE, TESS, Kepler/K2 data along with CHEOPS data in pycheops.

Last updated: Oct. 24, 2023

Code Language(s): Python3

pycdata: A module to import datasets from various instruments in pycheops

Jayshil A. Patel, Alexis Brandeker, Pierre Maxted

copy_img
https://emac.gsfc.nasa.gov?cid=2310-006
2310-006

pycdata is a module to import datasets from various telescopes/instruments in pycheops. pycheops is a tool specifically designed to model CHEOPS observations of transits, eclipses and phase curves. While being a genius tool, what it lacks is a facility to model datasets from other telescopes/instruments, even the PSF photometry produced by PIPE. pycdata can be used to import datasets from PIPE, TESS and Kepler/K2 in pycheops thus enabling a joint lightcurve analysis of PIPE, TESS, Kepler/K2 data along with CHEOPS data in pycheops.

About
ExoMDN: Rapid Characterization of Exoplanet Interiors with Mixture Density Networks

Baumeister, P. and Tosi, N.

EMAC: 2310-005 EMAC 2310-005
copy_img
https://emac.gsfc.nasa.gov?cid=2310-005

ExoMDN is a machine-learning-based exoplanet interior inference model using Mixture Density Networks. The model is trained on more than 5.6 million synthetic planet interior structures. Given mass, radius, and equilibrium temperature, ExoMDN is capable of providing a full inference of the interior structure of low-mass exoplanets in under a second without the need for a dedicated interior model.

Last updated: Oct. 19, 2023

Code Language(s): Python3

ExoMDN: Rapid Characterization of Exoplanet Interiors with Mixture Density Networks

Baumeister, P. and Tosi, N.

copy_img
https://emac.gsfc.nasa.gov?cid=2310-005
2310-005

ExoMDN is a machine-learning-based exoplanet interior inference model using Mixture Density Networks. The model is trained on more than 5.6 million synthetic planet interior structures. Given mass, radius, and equilibrium temperature, ExoMDN is capable of providing a full inference of the interior structure of low-mass exoplanets in under a second without the need for a dedicated interior model.

About
Exo_Transmit with Tholin Opacities

Corrales, Lia; Gavilan, Lisseth; Teal, D. J.; Kempton, E. M. R.

EMAC: 2310-004 EMAC 2310-004
copy_img
https://emac.gsfc.nasa.gov?cid=2310-004

A static, refactored version of Exo_Transmit (Kempton et al. 2017, Teal et al. 2022, Corrales et al. 2023) for computing exoplanet transmission spectra with the new tholin species. This code uses optical constants from tholins grown in the laboratory and computed cross-sections (Mie) for a wide range of particle sizes, for wavelengths of 0.13-10 micron.

Last updated: Oct. 17, 2023

Code Language(s): C

Exo_Transmit with Tholin Opacities

Corrales, Lia; Gavilan, Lisseth; Teal, D. J.; Kempton, E. M. R.

copy_img
https://emac.gsfc.nasa.gov?cid=2310-004
2310-004

A static, refactored version of Exo_Transmit (Kempton et al. 2017, Teal et al. 2022, Corrales et al. 2023) for computing exoplanet transmission spectra with the new tholin species. This code uses optical constants from tholins grown in the laboratory and computed cross-sections (Mie) for a wide range of particle sizes, for wavelengths of 0.13-10 micron.

Demo
PCM_LBL: Planetary Climate Model (Line-By-Line)

Wordsworth, Robin et al.

EMAC: 2310-003 EMAC 2310-003
copy_img
https://emac.gsfc.nasa.gov?cid=2310-003

PCM_LBL is a 1D radiative-convective code designed to simulate the climates of diverse planetary atmospheres, from present-day Earth to early Mars and exoplanets. The code is written in modular modern Fortran and uses a 'brute-force' spectral approach where absorption coefficients are computed on a fixed spectral grid directly from line data. This allows climate calculations to be performed more simply and at higher accuracy than in a correlated-k approach.

Last updated: Oct. 17, 2023

Code Language(s): Fortran 90

PCM_LBL: Planetary Climate Model (Line-By-Line)

Wordsworth, Robin et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2310-003
2310-003

PCM_LBL is a 1D radiative-convective code designed to simulate the climates of diverse planetary atmospheres, from present-day Earth to early Mars and exoplanets. The code is written in modular modern Fortran and uses a 'brute-force' spectral approach where absorption coefficients are computed on a fixed spectral grid directly from line data. This allows climate calculations to be performed more simply and at higher accuracy than in a correlated-k approach.

About
GEOCLIM: Global Silicate Weathering Estimation

Baum, Mark; Fu, Minmin

EMAC: 2310-002 EMAC 2310-002
copy_img
https://emac.gsfc.nasa.gov?cid=2310-002

This module replicates some features of the GEOCLIM model, originally written in Fortran, but now in Julia to make them easier to use. The module implements these formulations to estimate global silicate weathering rates from gridded climatology, typically taken from the results of a global climate model like CCSM or FOAM. It is intended to estimate weathering during periods of Earth history when the continental configuration was radically different, typically more than 100 million years ago. For more information about the original GEOCLIM, see the Methods/Supplement of Goddéris et al.

Last updated: Oct. 17, 2023

Code Language(s): Julia

GEOCLIM: Global Silicate Weathering Estimation

Baum, Mark; Fu, Minmin

copy_img
https://emac.gsfc.nasa.gov?cid=2310-002
2310-002

This module replicates some features of the GEOCLIM model, originally written in Fortran, but now in Julia to make them easier to use. The module implements these formulations to estimate global silicate weathering rates from gridded climatology, typically taken from the results of a global climate model like CCSM or FOAM. It is intended to estimate weathering during periods of Earth history when the continental configuration was radically different, typically more than 100 million years ago. For more information about the original GEOCLIM, see the Methods/Supplement of Goddéris et al.

About
photoevolver

Fernández, Jorge

EMAC: 2310-001 EMAC 2310-001
copy_img
https://emac.gsfc.nasa.gov?cid=2310-001

photoevolver is a Python module that evolves the gaseous envelope of planets backwards and forward in time, taking into account internal structure and cooling rate, atmospheric mass loss processes, and the stellar X-ray emission history.

Last updated: Oct. 13, 2023

Code Language(s): Python3, C

photoevolver

Fernández, Jorge

copy_img
https://emac.gsfc.nasa.gov?cid=2310-001
2310-001

photoevolver is a Python module that evolves the gaseous envelope of planets backwards and forward in time, taking into account internal structure and cooling rate, atmospheric mass loss processes, and the stellar X-ray emission history.

About
smart: Spectral Modeling Analysis and RV Tool

Hsu, Chih-Chun; Burgasser, Adam; Theissen, Chris; Birky, Jessica

EMAC: 2309-001 EMAC 2309-001
copy_img
https://emac.gsfc.nasa.gov?cid=2309-001

The smart is a Markov Chain Monte Carlo (MCMC) forward-modeling framework for spectroscopic data, currently working for high-resolution spectrometers including Keck/NIRSPEC, SDSS/APOGEE, Gemini/IGRINS, Lick/HPF, Keck/HIRES and medium-resolution spectrometers including Keck/OSIRIS and Keck/NIRES. For NIRSPEC users, required adjustments need to be made before reducing private data using NIRSPEC-Data-Reduction-Pipeline(NSDRP), to perform telluric wavelength calibrations, and to forward model spectral data. The code is currently being developed.

Last updated: Sep. 29, 2023

Code Language(s): Python3

smart: Spectral Modeling Analysis and RV Tool

Hsu, Chih-Chun; Burgasser, Adam; Theissen, Chris; Birky, Jessica

copy_img
https://emac.gsfc.nasa.gov?cid=2309-001
2309-001

The smart is a Markov Chain Monte Carlo (MCMC) forward-modeling framework for spectroscopic data, currently working for high-resolution spectrometers including Keck/NIRSPEC, SDSS/APOGEE, Gemini/IGRINS, Lick/HPF, Keck/HIRES and medium-resolution spectrometers including Keck/OSIRIS and Keck/NIRES. For NIRSPEC users, required adjustments need to be made before reducing private data using NIRSPEC-Data-Reduction-Pipeline(NSDRP), to perform telluric wavelength calibrations, and to forward model spectral data. The code is currently being developed.

About
CROCODILE: CROss-COrrelation retrievals of Directly-Imaged self-Luminous Exoplanets

Hayoz, J. et al. 2023

EMAC: 2308-001 EMAC 2308-001
copy_img
https://emac.gsfc.nasa.gov?cid=2308-001

CROCODILE provides the statistical framework to interpret the three main observables of directly-imaged exoplanetary atmospheres, namely photometry, low-resolution spectroscopy, and medium (and higher) resolution cross-correlation spectroscopy. These will be measured by the next generation of instruments such as ERIS at the Very Large Telescope, MIRI aboard the James Webb Space Telescope, and METIS at the future Extremely Large Telescope.

Last updated: Aug. 2, 2023

Code Language(s): Python

CROCODILE: CROss-COrrelation retrievals of Directly-Imaged self-Luminous Exoplanets

Hayoz, J. et al. 2023

copy_img
https://emac.gsfc.nasa.gov?cid=2308-001
2308-001

CROCODILE provides the statistical framework to interpret the three main observables of directly-imaged exoplanetary atmospheres, namely photometry, low-resolution spectroscopy, and medium (and higher) resolution cross-correlation spectroscopy. These will be measured by the next generation of instruments such as ERIS at the Very Large Telescope, MIRI aboard the James Webb Space Telescope, and METIS at the future Extremely Large Telescope.

About
mr-plotter: Mass-Radius Diagrams Plotter

A. Castro-González, J. Lillo-Box

EMAC: 2307-001 EMAC 2307-001
copy_img
https://emac.gsfc.nasa.gov?cid=2307-001

Mister plotter (mr-plotter) is a user-friendly Python tool that creates paper-ready mass-radius diagrams with your favorite theoretical models. It also includes the ability to color-code diagrams based on any published stellar or planetary property collected in the NASA Exoplanet Archive.

Last updated: Jul. 5, 2023

Code Language(s): Python3

mr-plotter: Mass-Radius Diagrams Plotter

A. Castro-González, J. Lillo-Box

copy_img
https://emac.gsfc.nasa.gov?cid=2307-001
2307-001

Mister plotter (mr-plotter) is a user-friendly Python tool that creates paper-ready mass-radius diagrams with your favorite theoretical models. It also includes the ability to color-code diagrams based on any published stellar or planetary property collected in the NASA Exoplanet Archive.

About
MAGIC: Microlensing Analysis Guided by Intelligent Computation

Haimeng Zhao and Wei Zhu

EMAC: 2306-004 EMAC 2306-004
copy_img
https://emac.gsfc.nasa.gov?cid=2306-004

MAGIC is a machine learning framework to efficiently and accurately infer the microlensing parameters of binary events with realistic data quality. In MAGIC, binary microlensing parameters are divided into two groups and inferred separately with different neural networks. The key feature of MAGIC is the introduction of neural controlled differential equation, which provides the capability to handle light curves with irregular sampling and large data gaps. MAGIC is able to locate degenerate solutions in real events even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, it also has implications to other studies that involve time series.

Last updated: Jun. 15, 2023

Code Language(s): Python3

MAGIC: Microlensing Analysis Guided by Intelligent Computation

Haimeng Zhao and Wei Zhu

copy_img
https://emac.gsfc.nasa.gov?cid=2306-004
2306-004

MAGIC is a machine learning framework to efficiently and accurately infer the microlensing parameters of binary events with realistic data quality. In MAGIC, binary microlensing parameters are divided into two groups and inferred separately with different neural networks. The key feature of MAGIC is the introduction of neural controlled differential equation, which provides the capability to handle light curves with irregular sampling and large data gaps. MAGIC is able to locate degenerate solutions in real events even when large data gaps are introduced. As irregular samplings are common in astronomical surveys, it also has implications to other studies that involve time series.

About
SWAMPE: A Shallow-Water Atmospheric Model in Python for Exoplanets

Landgren, E. and Nadeau, A.

EMAC: 2306-003 EMAC 2306-003
copy_img
https://emac.gsfc.nasa.gov?cid=2306-003

SWAMPE is a Python package for modeling the dynamics of exoplanetary atmospheres. SWAMPE is an intermediate-complexity, two-dimensional shallow-water general circulation model. Benchmarked for synchronously rotating hot Jupiters and sub-Neptunes, the code is modular and could be easily modified to model dissimilar space objects, from Brown Dwarfs to terrestrial, potentially habitable exoplanets. SWAMPE can be easily run on a personal laptop.

Last updated: Jun. 6, 2023

Code Language(s): Python

SWAMPE: A Shallow-Water Atmospheric Model in Python for Exoplanets

Landgren, E. and Nadeau, A.

copy_img
https://emac.gsfc.nasa.gov?cid=2306-003
2306-003

SWAMPE is a Python package for modeling the dynamics of exoplanetary atmospheres. SWAMPE is an intermediate-complexity, two-dimensional shallow-water general circulation model. Benchmarked for synchronously rotating hot Jupiters and sub-Neptunes, the code is modular and could be easily modified to model dissimilar space objects, from Brown Dwarfs to terrestrial, potentially habitable exoplanets. SWAMPE can be easily run on a personal laptop.

About
Bioverse: Simulation framework for Bayesian hypothesis testing of statistical exoplanet missions

Alex Bixel et al.

EMAC: 2306-002 EMAC 2306-002
copy_img
https://emac.gsfc.nasa.gov?cid=2306-002

Bioverse is a quantitative framework for assessing the diagnostic power of a statistical exoplanet survey. It combines Gaia-based stellar samples with Kepler-derived exoplanet demographics and a mission simulator that enables exploration of a variety of observing, follow-up, and characterization strategies. Uniquely, Bioverse contains a versatile module for population-level hypothesis testing supporting trade studies and survey optimization. It currently supports direct imaging or transit missions, but its modularity makes it adaptable to any mission concept that makes measurements on a sample of exoplanets.

Last updated: Jun. 5, 2023

Code Language(s): Python3

Bioverse: Simulation framework for Bayesian hypothesis testing of statistical exoplanet missions

Alex Bixel et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2306-002
2306-002

Bioverse is a quantitative framework for assessing the diagnostic power of a statistical exoplanet survey. It combines Gaia-based stellar samples with Kepler-derived exoplanet demographics and a mission simulator that enables exploration of a variety of observing, follow-up, and characterization strategies. Uniquely, Bioverse contains a versatile module for population-level hypothesis testing supporting trade studies and survey optimization. It currently supports direct imaging or transit missions, but its modularity makes it adaptable to any mission concept that makes measurements on a sample of exoplanets.

About
TurbospectrumNLTE: Synthetic stellar spectra calculator LTE / NLTE

Plez, Bertrand; Gerber, Jeff; Magg, Ekaterina; Bergemann, Maria

EMAC: 2306-001 EMAC 2306-001
copy_img
https://emac.gsfc.nasa.gov?cid=2306-001

Latest version of TS (Turbospectrum), with NLTE capabilities. Computation of stellar spectra (flux and intensities) in 1D or average <3D> stellar atmosphere models. In order to compute NLTE stellar spectra, additional data is needed, downloadable outside GitHub. See documentation in DOC folder Python wrappers are available at https://github.com/EkaterinaSe/TurboSpectrum-Wrapper/ and https://github.com/JGerbs13/TSFitPy They allow interpolation between models and fitting of spectra to derive stellar parameters.

Last updated: Jun. 5, 2023

Code Language(s): Fortran, Python3

TurbospectrumNLTE: Synthetic stellar spectra calculator LTE / NLTE

Plez, Bertrand; Gerber, Jeff; Magg, Ekaterina; Bergemann, Maria

copy_img
https://emac.gsfc.nasa.gov?cid=2306-001
2306-001

Latest version of TS (Turbospectrum), with NLTE capabilities. Computation of stellar spectra (flux and intensities) in 1D or average <3D> stellar atmosphere models. In order to compute NLTE stellar spectra, additional data is needed, downloadable outside GitHub. See documentation in DOC folder Python wrappers are available at https://github.com/EkaterinaSe/TurboSpectrum-Wrapper/ and https://github.com/JGerbs13/TSFitPy They allow interpolation between models and fitting of spectra to derive stellar parameters.

About
Applefy: Robust detection limits for high-contrast imaging

Bonse, Markus J.; Gebhard, Timothy D.

EMAC: 2305-003 EMAC 2305-003
copy_img
https://emac.gsfc.nasa.gov?cid=2305-003

Applefy calculates detection limits for exoplanet high contrast imaging (HCI) datasets. The package provides a number of features and functionalities to improve the accuracy and robustness of contrast curve calculations. Applefy implements the classical approach based on the t-test as well as the parametric boostrap test for non-Gaussian residual noise. Written in Python, it computes contrast curves and contrast grids.

Last updated: May. 26, 2023

Code Language(s): Python

Applefy: Robust detection limits for high-contrast imaging

Bonse, Markus J.; Gebhard, Timothy D.

copy_img
https://emac.gsfc.nasa.gov?cid=2305-003
2305-003

Applefy calculates detection limits for exoplanet high contrast imaging (HCI) datasets. The package provides a number of features and functionalities to improve the accuracy and robustness of contrast curve calculations. Applefy implements the classical approach based on the t-test as well as the parametric boostrap test for non-Gaussian residual noise. Written in Python, it computes contrast curves and contrast grids.

About
Tiberius: Time series spectral extraction and transit light curve fitting

Kirk, J.

EMAC: 2305-002 EMAC 2305-002
copy_img
https://emac.gsfc.nasa.gov?cid=2305-002

Tiberius is a Python library for reducing time series spectra and fitting exoplanet transit light curves. This can be used to extract spectra from JWST (all 4 instruments), along with ground-based long-slit spectrographs and Keck/NIRSPEC echelle spectra (beta). The light curve fitting routines can be used as as standalone to fit, for example, HST light curves extracted with other methods.

Last updated: May. 22, 2023

Code Language(s): Python

Tiberius: Time series spectral extraction and transit light curve fitting

Kirk, J.

copy_img
https://emac.gsfc.nasa.gov?cid=2305-002
2305-002

Tiberius is a Python library for reducing time series spectra and fitting exoplanet transit light curves. This can be used to extract spectra from JWST (all 4 instruments), along with ground-based long-slit spectrographs and Keck/NIRSPEC echelle spectra (beta). The light curve fitting routines can be used as as standalone to fit, for example, HST light curves extracted with other methods.

About
light-curve: Irregular time series analysis toolbox for Rust and Python

Malanchev, K.; Lavrukhina, A.

EMAC: 2305-001 EMAC 2305-001
copy_img
https://emac.gsfc.nasa.gov?cid=2305-001

The "light-curve" project is a toolbox for analyzing irregular time-series data, consisting of two components: a feature extractor and a dm-dt mapper. Feature extraction is available as Rust and Python libraries, including various feature extractors like magnitude statistics, shape-based features, Lomb-Scargle periodogram peaks, and parametric fits. The dm-dt mapper represents observation pairs as 2-D points based on magnitude and time differences, available in the same Python library, the Rust library, and a binary executable for generating PNG images.

Last updated: May. 4, 2023

Code Language(s): Python, Rust

light-curve: Irregular time series analysis toolbox for Rust and Python

Malanchev, K.; Lavrukhina, A.

copy_img
https://emac.gsfc.nasa.gov?cid=2305-001
2305-001

The "light-curve" project is a toolbox for analyzing irregular time-series data, consisting of two components: a feature extractor and a dm-dt mapper. Feature extraction is available as Rust and Python libraries, including various feature extractors like magnitude statistics, shape-based features, Lomb-Scargle periodogram peaks, and parametric fits. The dm-dt mapper represents observation pairs as 2-D points based on magnitude and time differences, available in the same Python library, the Rust library, and a binary executable for generating PNG images.

About Demo
ExoCcycleGeo: Geophysical and geochemical controls on abiotic carbon cycling on Earth-like planets

Neveu, M. et al.

EMAC: 2302-005 EMAC 2302-005
copy_img
https://emac.gsfc.nasa.gov?cid=2302-005

This model of geological carbon fluxes leverages widely used geochemical codes of solid-melt equilibria for silicate rocks (MELTS) and of equilibria and kinetics of water-rock interactions (PHREEQC). Coupled with a simple numerical computation of global thermal evolution, this model enables investigation of the effects of planet size (mass) and bulk, surface, and upper mantle composition on carbon cycling through geologic time. Its applicable size range (0.5 to 2 Earth masses) is limited by the fidelity of the geodynamic model. The applicable range of compositions is limited by those that can be handled by MELTS and PHREEQC.

Last updated: Feb. 27, 2023

Code Language(s): C

ExoCcycleGeo: Geophysical and geochemical controls on abiotic carbon cycling on Earth-like planets

Neveu, M. et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2302-005
2302-005

This model of geological carbon fluxes leverages widely used geochemical codes of solid-melt equilibria for silicate rocks (MELTS) and of equilibria and kinetics of water-rock interactions (PHREEQC). Coupled with a simple numerical computation of global thermal evolution, this model enables investigation of the effects of planet size (mass) and bulk, surface, and upper mantle composition on carbon cycling through geologic time. Its applicable size range (0.5 to 2 Earth masses) is limited by the fidelity of the geodynamic model. The applicable range of compositions is limited by those that can be handled by MELTS and PHREEQC.

About Demo
MATRIX ToolKit: ToolKit for Multi-phAse Transits Recovery from Injected eXoplanets

Dévora-Pajares, Martín & Pozuelos, Francisco J.

EMAC: 2302-004 EMAC 2302-004
copy_img
https://emac.gsfc.nasa.gov?cid=2302-004

The MATRIX ToolKit has been specially designed to establish detection limits of photometry data sets by performing robust injection-and-recovery analyses on a three dimensional grid of scenarios (orbital period vs planetary radius vs transit epoch). This kind of scientific detection threshold determination can now be done with a simple python command with the significant addition of taking into account different transit epochs, which helps to establish a more reliable detection rate for a given period and radius.

Last updated: Feb. 17, 2023

Code Language(s): Python3

MATRIX ToolKit: ToolKit for Multi-phAse Transits Recovery from Injected eXoplanets

Dévora-Pajares, Martín & Pozuelos, Francisco J.

copy_img
https://emac.gsfc.nasa.gov?cid=2302-004
2302-004

The MATRIX ToolKit has been specially designed to establish detection limits of photometry data sets by performing robust injection-and-recovery analyses on a three dimensional grid of scenarios (orbital period vs planetary radius vs transit epoch). This kind of scientific detection threshold determination can now be done with a simple python command with the significant addition of taking into account different transit epochs, which helps to establish a more reliable detection rate for a given period and radius.

About
WATSON: Visual Vetting and Analysis of Transits from Space ObservatioNs

Dévora-Pajares, M.

EMAC: 2302-003 EMAC 2302-003
copy_img
https://emac.gsfc.nasa.gov?cid=2302-003

WATSON (Visual Vetting and Analysis of Transits from Space ObservatioNs is a lightweight software package that enables a comfortable visual vetting of a transiting signal candidate from Kepler, K2 and TESS missions. WATSON looks for transit-like signals that could be generated by other sources or instrument artifacts. The code runs simplified tests on scenarios including:

  • Transit shape model fit
  • Odd-even transits checks
  • Centroids shifts
  • Optical ghost effects
  • Transit source offsets
  • and more...
With these data, we compute metrics to alert scientists about problematic signals.

Last updated: Feb. 17, 2023

Code Language(s): Python3

WATSON: Visual Vetting and Analysis of Transits from Space ObservatioNs

Dévora-Pajares, M.

copy_img
https://emac.gsfc.nasa.gov?cid=2302-003
2302-003

WATSON (Visual Vetting and Analysis of Transits from Space ObservatioNs is a lightweight software package that enables a comfortable visual vetting of a transiting signal candidate from Kepler, K2 and TESS missions. WATSON looks for transit-like signals that could be generated by other sources or instrument artifacts. The code runs simplified tests on scenarios including:

  • Transit shape model fit
  • Odd-even transits checks
  • Centroids shifts
  • Optical ghost effects
  • Transit source offsets
  • and more...
With these data, we compute metrics to alert scientists about problematic signals.

About
PEPITA: Prediction of Exoplanet Transit Parameters Precisions using Information Analysis Techniques

Julio Hernandez Camero

EMAC: 2302-002 EMAC 2302-002
copy_img
https://emac.gsfc.nasa.gov?cid=2302-002

PEPITA is a Python package that allows making predictions for the precision of exoplanet parameters using transit light-curves, without the need of performing a fit to the data. Behind scenes, it makes use of the Information Analysis techniques to predict the best precision that can be obtained by fitting a light-curve without actually needing to perform the fit.

Last updated: Feb. 14, 2023

Code Language(s): Python3

PEPITA: Prediction of Exoplanet Transit Parameters Precisions using Information Analysis Techniques

Julio Hernandez Camero

copy_img
https://emac.gsfc.nasa.gov?cid=2302-002
2302-002

PEPITA is a Python package that allows making predictions for the precision of exoplanet parameters using transit light-curves, without the need of performing a fit to the data. Behind scenes, it makes use of the Information Analysis techniques to predict the best precision that can be obtained by fitting a light-curve without actually needing to perform the fit.

About
PyMieScatt: The Python Mie Scattering package

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

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

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

PyMieScatt: The Python Mie Scattering package

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

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

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!

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

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

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

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

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

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

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

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.

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

Zieba, Sebastian and Kreidberg, Laura

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

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

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

Zieba, Sebastian and Kreidberg, Laura

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

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.

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

A. García Muñoz

EMAC: 2212-005 EMAC 2212-005
copy_img
https://emac.gsfc.nasa.gov?cid=2212-005

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

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

A. García Muñoz

copy_img
https://emac.gsfc.nasa.gov?cid=2212-005
2212-005

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.

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

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

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

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

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

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

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

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.

About
Astroquery: Library with tools to query astronomical databases

Ginsburg, Sipőcz, Brasseur et al.

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

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

Astroquery: Library with tools to query astronomical databases

Ginsburg, Sipőcz, Brasseur et al.

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

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.

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

JWST ERS 1386 Team

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

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

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

JWST ERS 1386 Team

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

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.

About Demo
Butterpy: realistic star spot evolution and light curves in Python

Claytor, Zachary R. et al.

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

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

Butterpy: realistic star spot evolution and light curves in Python

Claytor, Zachary R. et al.

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

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."

Kiauhoku: Python utilities for stellar model grid interpolation

Claytor, Zachary R. et al.

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

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

Kiauhoku: Python utilities for stellar model grid interpolation

Claytor, Zachary R. et al.

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

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.

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

T. Robinson

EMAC: 2211-006 EMAC 2211-006
copy_img
https://emac.gsfc.nasa.gov?cid=2211-006

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

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

T. Robinson

copy_img
https://emac.gsfc.nasa.gov?cid=2211-006
2211-006

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.

About Demo
Molecfit: A general tool for telluric absorption correction

Smette et al., Kausch et al.

EMAC: 2211-005 EMAC 2211-005
copy_img
https://emac.gsfc.nasa.gov?cid=2211-005

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

Molecfit: A general tool for telluric absorption correction

Smette et al., Kausch et al.

copy_img
https://emac.gsfc.nasa.gov?cid=2211-005
2211-005

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.

Pyshellspec: Binary systems with circumstellar matter (β Lyrae)

Brož, M., Nemravová, J.

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

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

Pyshellspec: Binary systems with circumstellar matter (β Lyrae)

Brož, M., Nemravová, J.

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

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).

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

Yair Judkovsky, Aviv Ofir and Oded Aharonson

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

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

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

Yair Judkovsky, Aviv Ofir and Oded Aharonson

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

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.

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

Aviv Ofir

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

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

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

Aviv Ofir

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

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.

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

Eden Rein and Aviv Ofir

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

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

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

Eden Rein and Aviv Ofir

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

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.

About
PySME: Stellar Spectral Synthesis and Parameter Fitting

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

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

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

PySME: Stellar Spectral Synthesis and Parameter Fitting

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

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

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).

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

Antoine Darveau-Bernier et al.

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

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

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

Antoine Darveau-Bernier et al.

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

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.

About
FORECAST: Finely Optimised REtrieval of Companions of Accelerating STars

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

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

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

FORECAST: Finely Optimised REtrieval of Companions of Accelerating STars

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

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

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.

Exo-DMC: Exoplanet Detection Map Calculator

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

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

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

Exo-DMC: Exoplanet Detection Map Calculator

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

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

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.

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

Michael Gully-Santiago & Caroline V. Morley

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

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

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

Michael Gully-Santiago & Caroline V. Morley

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

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

Matthias Y. He

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

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

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

Matthias Y. He

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

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.

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

Matthias Y. He

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

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

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

Matthias Y. He

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

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
RAPOC: Rosseland And Planck Opacity Converter

Lorenzo V. Mugnai and Darius Modirrousta-Galian

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

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

RAPOC: Rosseland And Planck Opacity Converter

Lorenzo V. Mugnai and Darius Modirrousta-Galian

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

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

The Community Coordinated Modeling Center at NASA GSFC

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

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

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

The Community Coordinated Modeling Center at NASA GSFC

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

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
ECLIPS3D: Public code for linear wave and circulation calculations

F. Debras et al.

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

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

ECLIPS3D: Public code for linear wave and circulation calculations

F. Debras et al.

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

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
TESS-SIP: TESS Systematics Insensitive Periodogram

Hedges et al.

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

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):

TESS-SIP: TESS Systematics Insensitive Periodogram

Hedges et al.

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

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.

About
IGRINS RV: A Radial Velocity Pipeline for IGRINS

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

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

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):

IGRINS RV: A Radial Velocity Pipeline for IGRINS

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

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

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.

About
AccretR: A planetary accretion and composition code in R

Mohit Melwani Daswani

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

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

AccretR: A planetary accretion and composition code in R

Mohit Melwani Daswani

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

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.

About
FastChem: Ultra-fast Equilibrium Chemistry

Daniel Kitzmann, Joachim Stock

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

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

FastChem: Ultra-fast Equilibrium Chemistry

Daniel Kitzmann, Joachim Stock

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

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.

About
Staralt: Calculating Object Visibility for Ground-based Telescopes

Isaac Newton Group of Telescopes

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

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

Staralt: Calculating Object Visibility for Ground-based Telescopes

Isaac Newton Group of Telescopes

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

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).

ExoAtmospheres: IAC community database for exoplanet atmospheric observations

The Exoplanets and Astrobiology group at IAC

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

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

ExoAtmospheres: IAC community database for exoplanet atmospheric observations

The Exoplanets and Astrobiology group at IAC

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

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.

About Demo
THAI: TRAPPIST Habitable Atmosphere Intercomparison GCM Data Repository

THAI Team (T. Fauchez et al.)

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

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

THAI: TRAPPIST Habitable Atmosphere Intercomparison GCM Data Repository

THAI Team (T. Fauchez et al.)

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

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.

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

Luca Cacciapuoti et al.

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

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

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

Luca Cacciapuoti et al.

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

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

Prose: A Python framework for modular astronomical images processing

Lionel Garcia

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

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

Prose: A Python framework for modular astronomical images processing

Lionel Garcia

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

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.

About Demo
exoVista: Planetary System Models for Survey Analyses

Christopher Stark

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

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

exoVista: Planetary System Models for Survey Analyses

Christopher Stark

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

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.

M_-M_K-: Estimating realistic stellar masses from magnitudes

Andrew Mann et al.

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

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

M_-M_K-: Estimating realistic stellar masses from magnitudes

Andrew Mann et al.

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

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.

About
DustPy: A Python Package for Dust Evolution in Protoplanetary Disks

Sebastian Markus Stammler ; Tilman Birnstiel

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

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

DustPy: A Python Package for Dust Evolution in Protoplanetary Disks

Sebastian Markus Stammler ; Tilman Birnstiel

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

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.

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

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

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

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

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

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

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

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.

About
VPLanet: The Virtual Planet Simulator

Rory Barnes et al.

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

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

VPLanet: The Virtual Planet Simulator

Rory Barnes et al.

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

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.

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

Bell, T. J. et al.

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

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

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

Bell, T. J. et al.

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

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.

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

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

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

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