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

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

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

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

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

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

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

Click here to find out about our first ever EMAC Workshop next February!
EMAC: 2209-015 EMAC 2209-015
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SysSimPyPlots: Loading, analyzing, and plotting catalogs generated from the SysSim models

Matthias Y. He

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

Code Language(s): Python3

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

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

Matthias Y. He

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

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

Matthias Y. He

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

Code Language(s): Python3

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

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

Matthias Y. He

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

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

Lorenzo V. Mugnai and Darius Modirrousta-Galian

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

Code Language(s): Python3

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

RAPOC: Rosseland And Planck Opacity Converter

Lorenzo V. Mugnai and Darius Modirrousta-Galian

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

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

The Community Coordinated Modeling Center at NASA GSFC

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

Code Language(s): Python3

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

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

The Community Coordinated Modeling Center at NASA GSFC

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

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

F. Debras et al.

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

Code Language(s): Fortran

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

ECLIPS3D: Public code for linear wave and circulation calculations

F. Debras et al.

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

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

Hedges et al.

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

Code Language(s):

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

TESS-SIP: TESS Systematics Insensitive Periodogram

Hedges et al.

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

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

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

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

Code Language(s):

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

IGRINS RV: A Radial Velocity Pipeline for IGRINS

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

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

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

Mohit Melwani Daswani

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

Code Language(s): R

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

AccretR: A planetary accretion and composition code in R

Mohit Melwani Daswani

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

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

Daniel Kitzmann, Joachim Stock

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

Code Language(s): C++, Python3

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

FastChem: Ultra-fast Equilibrium Chemistry

Daniel Kitzmann, Joachim Stock

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

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

Isaac Newton Group of Telescopes

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

Code Language(s): PhP, Fortran

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

Staralt: Calculating Object Visibility for Ground-based Telescopes

Isaac Newton Group of Telescopes

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

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

The Exoplanets and Astrobiology group at IAC

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

Code Language(s): php

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

ExoAtmospheres: IAC community database for exoplanet atmospheric observations

The Exoplanets and Astrobiology group at IAC

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

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

THAI Team (T. Fauchez et al.)

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

Code Language(s): N/A

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

THAI: TRAPPIST Habitable Atmosphere Intercomparison GCM Data Repository

THAI Team (T. Fauchez et al.)

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

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EMAC: 2209-004 EMAC 2209-004
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The TESS Triple-9 (TT9) Catalog: 999 uniformly vetted TESS candidate exoplanets

Luca Cacciapuoti et al.

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

Code Language(s): Python

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

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

Luca Cacciapuoti et al.

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

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

Lionel Garcia

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

Code Language(s): Python3, LaTeX

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

Prose: A Python framework for modular astronomical images processing

Lionel Garcia

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

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

Christopher Stark

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

Code Language(s): IDL, C

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

exoVista: Planetary System Models for Survey Analyses

Christopher Stark

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

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

Andrew Mann et al.

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

Code Language(s): Python, IDL

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

M_-M_K-: Estimating realistic stellar masses from magnitudes

Andrew Mann et al.

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

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

Sebastian Markus Stammler ; Tilman Birnstiel

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

Code Language(s): Python3, Fortran

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

DustPy: A Python Package for Dust Evolution in Protoplanetary Disks

Sebastian Markus Stammler ; Tilman Birnstiel

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

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

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

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

Code Language(s): Fortran

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

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

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

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

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

Rory Barnes et al.

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

Code Language(s): C, Python3

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

VPLanet: The Virtual Planet Simulator

Rory Barnes et al.

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

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

Bell, T. J. et al.

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

Code Language(s): Python3

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

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

Bell, T. J. et al.

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

About
EMAC: 2207-001 EMAC 2207-001
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Pytmosph3R: Transmission/emission spectra from 3D atmospheric simulations (GCMs, ...)

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

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

Code Language(s): Python3

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

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

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

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

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

Hadden, Sam; Tamayo, Daniel

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

Code Language(s): Python3, C

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2207-002

celmech: A Python package for celestial mechanics

Hadden, Sam; Tamayo, Daniel

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

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EMAC: 2207-003 EMAC 2207-003
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pycheops: Python package for the analysis of light curves from the ESA CHEOPS mission

Maxted et al.

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

Code Language(s): Python3

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

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

Maxted et al.

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

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EMAC: 2207-004 EMAC 2207-004
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HELIOS-K: A GPU opacity calculator for exoplanetary atmospheres

Simon Grimm, Kevin Heng

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

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

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2207-004

HELIOS-K: A GPU opacity calculator for exoplanetary atmospheres

Simon Grimm, Kevin Heng

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

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EMAC: 2207-005 EMAC 2207-005
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l1periodogram: Searching for planetary signatures in radial velocity time-series with a sparse recovery technique

Nathan C. Hara

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

Code Language(s): Python3, Fortran

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2207-005

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

Nathan C. Hara

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

About Demo
EMAC: 2207-006 EMAC 2207-006
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Magnitude-squared coherence: Frequency-domain view of the cross-correlation between RV and activity indicator time series

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

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

Code Language(s): Python3

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

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

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

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

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EMAC: 2207-007 EMAC 2207-007
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MARGE: A Python package to train and evaluate neural networks

Himes et al.

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

Code Language(s): Python3

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

MARGE: A Python package to train and evaluate neural networks

Himes et al.

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

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EMAC: 2207-008 EMAC 2207-008
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HOMER: A Bayesian inverse modeling code

Himes et al.

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

Code Language(s): Python3

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

HOMER: A Bayesian inverse modeling code

Himes et al.

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

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EMAC: 2207-009 EMAC 2207-009
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Aladin: Aladin Sky Atlas

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

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

Code Language(s): java, javascript

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

Aladin: Aladin Sky Atlas

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

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

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EMAC: 2207-010 EMAC 2207-010
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FitsMap: A Simple, Lightweight Tool For Displaying Interactive Astronomical Image and Catalog Data

Ryan Hausen and Brant E. Robertson

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

Code Language(s): Python3, javascript

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

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

Ryan Hausen and Brant E. Robertson

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

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EMAC: 2207-011 EMAC 2207-011
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exocartographer: Forward-model for constraining exoplanet maps and orbital parameters from reflected lightcurves

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

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

Code Language(s): Python3

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

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

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

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

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EMAC: 2207-012 EMAC 2207-012
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SteParSyn: A Bayesian code to infer stellar atmospheric parameters using spectral synthesis

Tabernero, H. M. et al.

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

Code Language(s): Python3

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

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

Tabernero, H. M. et al.

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

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EMAC: 2207-013 EMAC 2207-013
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PyExoRaMa: An Interactive Tool in Python to Investigate the Radius-Mass Diagram for Exoplanets

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

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

Code Language(s): Python3

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

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

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

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

EMAC: 2207-014 EMAC 2207-014
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https://emac.gsfc.nasa.gov?cid=2207-014
special: SPEctral Characterization of ImAged Low-mass companions

Valentin Christiaens

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

Code Language(s): Python3

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

special: SPEctral Characterization of ImAged Low-mass companions

Valentin Christiaens

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

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EMAC: 2207-015 EMAC 2207-015
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GENGA: A GPU N-body integrator for planet formation and planetary system evolution

Simon Grimm, Joachim Stadel

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

Code Language(s): CUDA, C

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

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

Simon Grimm, Joachim Stadel

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

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EMAC: 2207-016 EMAC 2207-016
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https://emac.gsfc.nasa.gov?cid=2207-016
SOAP 2.0: RV stellar activity simulation including spot and faculae

X. Dumusque, I. Boisse, N.Santos

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

Code Language(s): C, Python2

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

SOAP 2.0: RV stellar activity simulation including spot and faculae

X. Dumusque, I. Boisse, N.Santos

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

About Demo
EMAC: 2207-017 EMAC 2207-017
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https://emac.gsfc.nasa.gov?cid=2207-017
NbodyGradient: Differentiable symplectic N-body code for arbitrary orbital architectures

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

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

Code Language(s): Julia

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

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

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

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

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EMAC: 2207-018 EMAC 2207-018
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https://emac.gsfc.nasa.gov?cid=2207-018
Roman Coronagraph Exposure Time Calculator: Estimates integration times for the Roman Coronagraph instrument

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

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

Code Language(s): Python3

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

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

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

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

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EMAC: 2207-019 EMAC 2207-019
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RASSINE: A tool for stellar spectrum continuum fitting

Michael Cretignier

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

Code Language(s): Python3

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

RASSINE: A tool for stellar spectrum continuum fitting

Michael Cretignier

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

About
EMAC: 2207-020 EMAC 2207-020
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SISTER: Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance

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

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

Code Language(s): Matlab

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

SISTER: Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance

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

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

About
EMAC: 2207-021 EMAC 2207-021
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spectools_ir: A suite of tools designed for analysis of medium/high-resolution IR molecular spectra

Colette Salyk

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

Code Language(s): Python3

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

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

Colette Salyk

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

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EMAC: 2207-022 EMAC 2207-022
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ROCKE-3D: A fully coupled ocean atmosphere 3-D General Circulation Model

Way et al. 2017

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

Code Language(s): Fortran

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

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

Way et al. 2017

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

About Demo
EMAC: 2207-023 EMAC 2207-023
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BASTA: The BAyesian STellar Algorithm

The BASTA team

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

Code Language(s): Python3, Fortran

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2207-023

BASTA: The BAyesian STellar Algorithm

The BASTA team

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

About
EMAC: 2207-024 EMAC 2207-024
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dips: Detrending strictly periodic signals

A. Prsa

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

Code Language(s): Python3

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2207-024

dips: Detrending strictly periodic signals

A. Prsa

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

About
EMAC: 2207-025 EMAC 2207-025
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RADIS: Fast Line-by-line code for infrared emission & absorption spectra at equilibrium & non-LTE

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

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

Code Language(s): Python3

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

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

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

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

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EMAC: 2207-026 EMAC 2207-026
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Isca: Idealized global circulation modeling: A flexible GCM for modelling planetary atmospheres.

The Isca Team

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

Code Language(s): Fortran, Python3

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https://emac.gsfc.nasa.gov?cid=2207-026
2207-026

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

The Isca Team

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

About
EMAC: 2207-027 EMAC 2207-027
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LDTk: Limb Darkening Toolkit

Hannu Parviainen

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

Code Language(s): Python3

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

LDTk: Limb Darkening Toolkit

Hannu Parviainen

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

About Demo
EMAC: 2207-028 EMAC 2207-028
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PyTransit: Fast and easy exoplanet transit light curve modelling in Python

Hannu Parviainen

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

Code Language(s): Python3

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

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

Hannu Parviainen

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

Demo
EMAC: 2207-029 EMAC 2207-029
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SERVAL: Spectrum radial velocity analyser

Mathias Zechmeister

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

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

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

SERVAL: Spectrum radial velocity analyser

Mathias Zechmeister

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

About
EMAC: 2207-030 EMAC 2207-030
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MAGRATHEA: Planetary interior structure code

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

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

Code Language(s): C++, Python3

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

MAGRATHEA: Planetary interior structure code

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

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

About
EMAC: 2207-031 EMAC 2207-031
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P-winds: An open-source Python code to model planetary outflows and upper atmospheres

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

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-031
2207-031

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

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

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

About Demo
EMAC: 2207-032 EMAC 2207-032
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TULIPS: Tool for Understanding the Lives, Interiors, and Physics of Stars

Laplace, E.

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

Code Language(s): Python3

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

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

Laplace, E.

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

About Demo
EMAC: 2207-033 EMAC 2207-033
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PyMieDAP: Radiative Transfer of Polarized Light in Planetary Atmospheres

Rossi, L. et al.

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

Code Language(s): Python3

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

PyMieDAP: Radiative Transfer of Polarized Light in Planetary Atmospheres

Rossi, L. et al.

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

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EMAC: 2207-034 EMAC 2207-034
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TidalPy: Software Toolbox for Estimating Tidal Heating and Dynamics in Solar System Moons and Exoplanets

Joe Renaud

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

Code Language(s): Python3

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

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

Joe Renaud

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

About
EMAC: 2207-035 EMAC 2207-035
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Nii: A Bayesian orbit retrieval code applied to differential astrometry

Sheng Jin

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

Code Language(s): Python3

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

Nii: A Bayesian orbit retrieval code applied to differential astrometry

Sheng Jin

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

EMAC: 2207-036 EMAC 2207-036
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EVolve: Growth and evolution of volcanically-derived atmospheres

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

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

Code Language(s): Python3, C++

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

EVolve: Growth and evolution of volcanically-derived atmospheres

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

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

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EMAC: 2207-037 EMAC 2207-037
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gCMCRT: A GPU accelerated MCRT code for (exo)planetary atmospheres

Elspeth K. H. Lee

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

Code Language(s): CUDA Fortran

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

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

Elspeth K. H. Lee

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

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EMAC: 2207-038 EMAC 2207-038
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Exodetbox: Underlying Methods for Calculating Integration Time Adjusted Completeness

Dean Keithly

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

Code Language(s): Python3

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

Exodetbox: Underlying Methods for Calculating Integration Time Adjusted Completeness

Dean Keithly

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

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EMAC: 2207-039 EMAC 2207-039
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ThERESA: Three-Dimensional Eclipse Mapping with Spectroscopic Lightcurves

Ryan C. Challener, Emily Rauscher

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

Code Language(s): Python3

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

ThERESA: Three-Dimensional Eclipse Mapping with Spectroscopic Lightcurves

Ryan C. Challener, Emily Rauscher

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

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EMAC: 2207-040 EMAC 2207-040
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Exo-REM: 1D self-consistent radiative-equilibrium model for exoplanetary atmospheres

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

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

Code Language(s): Python3, Fortran

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https://emac.gsfc.nasa.gov?cid=2207-040
2207-040

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

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

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

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EMAC: 2207-041 EMAC 2207-041
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UBER: Universal Boltzmann Equation Solver

Zheng, L. et al.

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

Code Language(s): C, Fortran, MATLAB

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2207-041

UBER: Universal Boltzmann Equation Solver

Zheng, L. et al.

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

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EMAC: 2207-042 EMAC 2207-042
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zeus: A Python implementation of ensemble slice sampling for efficient Bayesian parameter inference

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

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-042
2207-042

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

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

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

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EMAC: 2207-043 EMAC 2207-043
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The all-sky PLATO input catalogue

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

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

Code Language(s): N/A

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https://emac.gsfc.nasa.gov?cid=2207-043
2207-043

The all-sky PLATO input catalogue

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

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

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EMAC: 2207-044 EMAC 2207-044
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The Photometry Pipeline (PP): Automated photometry pipeline for small to medium-sized observatories

Michael Mommert

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-044
2207-044

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

Michael Mommert

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

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EMAC: 2207-045 EMAC 2207-045
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ExoInt: A devolatilization and interior modeling package for rocky planets

Haiyang S. Wang

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

Code Language(s): IDL, Python

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2207-045

ExoInt: A devolatilization and interior modeling package for rocky planets

Haiyang S. Wang

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

About
EMAC: 2207-046 EMAC 2207-046
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TRAN_K2: Search for planetary transits embedded in stellar variability and systematic effects

Geza Kovacs

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

Code Language(s): Fortran, Shell

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https://emac.gsfc.nasa.gov?cid=2207-046
2207-046

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

Geza Kovacs

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

About
EMAC: 2207-047 EMAC 2207-047
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PlanetPack: Command-line Tool for Radial Velocity and Transit Lightcurves Fitting

R.V. Baluev

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

Code Language(s): C++

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https://emac.gsfc.nasa.gov?cid=2207-047
2207-047

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

R.V. Baluev

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

EMAC: 2207-048 EMAC 2207-048
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https://emac.gsfc.nasa.gov?cid=2207-048
SMINT: Structure Model INTerpolator

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

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-048
2207-048

SMINT: Structure Model INTerpolator

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

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

About
EMAC: 2207-049 EMAC 2207-049
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TRIPPy: Python based Trailed Source Photometry

Wesley C. Fraser

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

Code Language(s): Python2, Python3

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https://emac.gsfc.nasa.gov?cid=2207-049
2207-049

TRIPPy: Python based Trailed Source Photometry

Wesley C. Fraser

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

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EMAC: 2207-050 EMAC 2207-050
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PEP: The Planetary Ephemeris Program

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

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

Code Language(s): Fortran

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https://emac.gsfc.nasa.gov?cid=2207-050
2207-050

PEP: The Planetary Ephemeris Program

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

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

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EMAC: 2207-051 EMAC 2207-051
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DYNAMITE: DYNAmical Multi-planet [System Architecture] Injection [via Monte Carlo Integrations] TEster

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

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-051
2207-051

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

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

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

EMAC: 2207-052 EMAC 2207-052
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VULCAN: Photochemical kinetics for planetary atmospheres

Shang-Min (Shami) Tsai

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

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

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https://emac.gsfc.nasa.gov?cid=2207-052
2207-052

VULCAN: Photochemical kinetics for planetary atmospheres

Shang-Min (Shami) Tsai

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

EMAC: 2207-053 EMAC 2207-053
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https://emac.gsfc.nasa.gov?cid=2207-053
APOLLO: MCMC Exoplanet Atmosphere Retrieval Code

Alex Howe & Arthur Adams

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

Code Language(s): C++, Python3

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https://emac.gsfc.nasa.gov?cid=2207-053
2207-053

APOLLO: MCMC Exoplanet Atmosphere Retrieval Code

Alex Howe & Arthur Adams

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

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EMAC: 2207-054 EMAC 2207-054
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IcyDwarf: IcyDwarf computes the coupled geophysical-chemical-orbital evolution of an icy dwarf planet or moon

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

IcyDwarf calculates:

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

Code Language(s): C

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https://emac.gsfc.nasa.gov?cid=2207-054
2207-054

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

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

IcyDwarf calculates:

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

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EMAC: 2207-148 EMAC 2207-148
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STARRY: Analytic Occultation Light Curves

Rodrigo Luger, Eric Agol, Daniel Foreman-Mackey, David P. Fleming, Jacob Lustig-Yaeger, Russell Deitrick

WEB TOOL DEVELOPMENT IN PROGRESS — The STARRY code package enables the computation of light curves for various applications in astronomy: transits and secondary eclipses of exoplanets, light curves of eclipsing binaries, rotational phase curves of exoplanets, light curves of planet-planet and planet-moon occultations, and more. By modeling celestial body surface maps as sums of spherical harmonics, STARRY does all this analytically and is therefore fast, stable, and differentiable. Coded in C++ but wrapped in Python, STARRY is easy to install and use.

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

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https://emac.gsfc.nasa.gov?cid=2207-148
2207-148

STARRY: Analytic Occultation Light Curves

Rodrigo Luger, Eric Agol, Daniel Foreman-Mackey, David P. Fleming, Jacob Lustig-Yaeger, Russell Deitrick

WEB TOOL DEVELOPMENT IN PROGRESS — The STARRY code package enables the computation of light curves for various applications in astronomy: transits and secondary eclipses of exoplanets, light curves of eclipsing binaries, rotational phase curves of exoplanets, light curves of planet-planet and planet-moon occultations, and more. By modeling celestial body surface maps as sums of spherical harmonics, STARRY does all this analytically and is therefore fast, stable, and differentiable. Coded in C++ but wrapped in Python, STARRY is easy to install and use.

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EMAC: 2207-055 EMAC 2207-055
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Deep-Transit: Identify Transit Signals with Deep Learning Based Object Detection Algorithm

Cui, K. et al.

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-055
2207-055

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

Cui, K. et al.

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

EMAC: 2207-056 EMAC 2207-056
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https://emac.gsfc.nasa.gov?cid=2207-056
ATES: ATmospheric EScape

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

Code Language(s): Fortran, Python3

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https://emac.gsfc.nasa.gov?cid=2207-056
2207-056

ATES: ATmospheric EScape

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

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EMAC: 2207-057 EMAC 2207-057
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EXOTIC (EXOplanet Transit Interpretation Code): A Python3 package for analyzing photometric data of transiting exoplanets

The Exoplanet Watch Team

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-057
2207-057

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

The Exoplanet Watch Team

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

About Demo
EMAC: 2207-058 EMAC 2207-058
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GRIT: A package for structure-preserving simulations of gravitationally interacting rigid-bodies

Chen, Renyi; Li, Gongjie and Tao, Molei

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

Code Language(s): C++

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https://emac.gsfc.nasa.gov?cid=2207-058
2207-058

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

Chen, Renyi; Li, Gongjie and Tao, Molei

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

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EMAC: 2207-059 EMAC 2207-059
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ExoPlaSim: Extending the Planet Simulator for Exoplanets

Paradise, A. et al.

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

Code Language(s): C, Fortran, Python3

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https://emac.gsfc.nasa.gov?cid=2207-059
2207-059

ExoPlaSim: Extending the Planet Simulator for Exoplanets

Paradise, A. et al.

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

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EMAC: 2207-060 EMAC 2207-060
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Hypatia Catalog: Database of stellar elemental abundances for solar neighborhood stars

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

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

Code Language(s): N/A

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https://emac.gsfc.nasa.gov?cid=2207-060
2207-060

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

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

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

About Demo
EMAC: 2207-061 EMAC 2207-061
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KOBE: Kepler Observes Bern Exoplanets

Lokesh Mishra

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-061
2207-061

KOBE: Kepler Observes Bern Exoplanets

Lokesh Mishra

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

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EMAC: 2207-062 EMAC 2207-062
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Pyratbay: A Forward-modeling and retrieval code to model exoplanet atmospheres and spectra

Cubillos, P. E. and Blecic, J.

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

Code Language(s): Python3

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https://emac.gsfc.nasa.gov?cid=2207-062
2207-062

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

Cubillos, P. E. and Blecic, J.

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

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EMAC: 2207-063 EMAC 2207-063
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SparseBLS: Box-Fitting Least Squares implementation for sparse data

Aviad Panahi, Shay Zucker

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

Code Language(s): Java8

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https://emac.gsfc.nasa.gov?cid=2207-063
2207-063

SparseBLS: Box-Fitting Least Squares implementation for sparse data

Aviad Panahi, Shay Zucker

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

About
EMAC: 2207-064 EMAC 2207-064
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RadVel: Radial Velocity Fitting Toolkit: General Toolkit for Modeling Radial Velocities

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

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

Code Language(s): Python2

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https://emac.gsfc.nasa.gov?cid=2207-064
2207-064

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

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

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

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EMAC: 2207-065 EMAC 2207-065
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https://emac.gsfc.nasa.gov?cid=2207-065
SpaceHub: A high-performance gravity integration toolkit for few-body problems in astrophysics

Yihan Wang, Nathan Leigh, Bin Liu and Rosalba Perna

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

Code Language(s): C++

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https://emac.gsfc.nasa.gov?cid=2207-065
2207-065

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

Yihan Wang, Nathan Leigh, Bin Liu and Rosalba Perna

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

About
EMAC: 2207-066