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 Bluesky account @exoplanetmodels.bsky.social (not an official NASA account), where new tools and features are highlighted.
- 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.
- 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.
EMAC has launched a new community-supported curator program, and we need your help! Check out our curator page to learn how exoplanet experts like yourself can support EMAC's mission, and help us spread the word about this new initiative!
The TESS Collection
Collection of tools related to data reduction, analysis, and model fitting specifically for TESS exoplanet transit and eclipse data
DART-Vetter: A Deep LeARning Tool for automatic vetting of candidates detected in transiting surveys
Fiscale, Stefano et al.
In the field of exoplanet detection, Machine Learning approaches have demonstrated their effectiveness in reliably classifying thousands of potential planetary signals detected by transit surveys, such as NASA's Kepler and TESS. However, existing models in the literature are typically trained to work on data from a single, specific mission. We introduce DART-Vetter, a Convolutional Neural Network designed to distinguish planetary transits from false positives of any transiting survey. By exclusively processing light curves, our model ensures ease of reproducibility and is lightweight enough to be executed on personal laptops.
SpExoDisks: Spectra of Exoplanet forming Disks
Wheeler, Caleb, III; Hinkel, Natalie R.; Banzatti, Andrea
The Spectra of Exoplanet-forming Disks (SpExoDisks) database and web portal (spexodisks.com) provides infrared spectra of protoplanetary disks. The spectra included in SpExoDisks are contributed by individual researchers who have either taken the observations or obtained the data from archives, and who provided the fully reduced 1-dimensional spectra. All spectra are transformed into a standard format inside SpExoDisks which includes additional information as available (program number, PI, date, and references for the data reduction and for acknowledging use of the data).
tglc (TESS-Gaia Light Curve): TESS FFI light curve based on point spread functions
Han & Brandt 2023
TESS-Gaia Light Curve (TGLC) is a PSF-based TESS full-frame image (FFI) light curve product. Using Gaia DR3 as priors, the team forward models the FFIs with the effective point spread function to remove contamination from nearby stars. The resulting light curves show a photometric precision closely tracking the pre-launch prediction of the noise level: TGLC's photometric precision consistently reaches ≲2% at 16th TESS magnitude even in crowded fields, demonstrating excellent decontamination and deblending power.
AstroNet: A Neural Network for Identifying Exoplanets in Light Curves
Shallue, Christopher J.
AstroNet is a Neural Network for identifying exoplanets in light curve data, implemented in TensorFlow.
It uses the Adam optimization algorithm to minimize the cross-entropy error function over the training set, and augments the training data by applying random horizontal reflections to the light curves during training. It also applies dropout regularization to the fully connected layers, which helps prevent overfitting by randomly “dropping” some of the output neurons from each layer during training to prevent the model from becoming overly reliant on any of its features.
AstroNet uses the Google-Vizier system for black-box optimization to automatically tune its hyper-parameters.
The Giants Pipeline: An open-source Python package to search for exoplanets around evolved stars
Saunders, Nicholas et al.
An open-source TESS FFI pipeline to access TESS data, produce noise-corrected light curves, and search for planets transiting evolved stars, with an emphasis on detecting planets around subgiant and RGB stars. The giants pipeline produces a one-page PDF summary for each target including the following vetting materials. Built with Lightkurve.
exovetter: Transiting exoplanet detection python vetter
The exovetter development team
The exovetter package provide statistical metrics and quick visualizations needed when evaluating a periodic transit found in time domain photometry, such as Kepler and TESS. This code wraps codes used to evaluate TESS, Kepler and K2 transit-like signals in order to remove obvious false positives.
TIKE: The Time Series Integrated Knowledge Engine
The MAST Team at STScI
The TIKE (Time series Integrated Knowledge Engine) is a new service being offered by STScI to support astronomers working with the time series data archived at MAST, such as data from NASA's TESS, Kepler and K2 missions. This tool is built on the Pangeo deployment of JupyterHub, using Kubernetes in AWS. TIKE is a platform where astronomers can make use of data science utilities, astronomy software, and community software packages to retrieve and analyze data sets without having to download the data to their machines or maintain their own set of python packages.
Difference Imaging Code and Visualization for TESS Full Frame Image Lightcurves
Oelkers, R. J. & Stassun, K.G.
This toolset includes a difference image analysis pipeline, which employs a delta-function kernel, useful for reducing TESS Full Frame Images. The data extracted using the pipeline for the first two years of TESS imagery is available for inspection at https://filtergraph.com/tess_ffi.
DACE: Data & Analysis Center for Exoplanets
Ségransan, D. et al.
The Data & Analysis Center for Exoplanets (DACE) is a PlanetS web-platform located at the University of Geneva (CH) dedicated to extrasolar planets data visualisation, exchange and analysis. DACE provides the research and education community with an enhanced access to exoplanet data with a suite of statistical tools for data analysis. Published observational data such as high resolution spectra, radial velocities, photometric light curves and high contrast imaging measurements are available online. Planetary systems formation and evolution can be studied as well as their long term dynamical evolution.
WATSON: Visual Vetting and Analysis of Transits from Space ObservatioNs
Dévora-Pajares, M.
WATSON (Visual Vetting and Analysis of Transits from Space ObservatioNs is a lightweight software package that enables a comfortable visual vetting of a transiting signal candidate from Kepler, K2 and TESS missions. WATSON looks for transit-like signals that could be generated by other sources or instrument artifacts. The code runs simplified tests on scenarios including:
- Transit shape model fit
- Odd-even transits checks
- Centroids shifts
- Optical ghost effects
- Transit source offsets
- and more...
SHERLOCK: Searching for Hints of Exoplanets fRom Lightcurves Of spaCe-based seeKers
Dévora-Pajares, M. & Pozuelos, F. J. et al.
SHERLOCK is an end-to-end pipeline that allows the users to explore the data from space-based missions to search for planetary candidates. It can be used to recover alerted candidates by the automatic pipelines such as SPOC and the QLP, the so-called Kepler objects of interest (KOIs) and TESS objects of interest (TOIs), and to search for candidates that remain unnoticed due to detection thresholds, lack of data exploration or poor photometric quality.
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.
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
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.
Astronet-Triage: A Neural Network for TESS Light Curve Triage
Yu, L. et al.
Astronet-Triage is a deep learning model capable of performing triage on TESS candidates. It is trained and tested on real TESS light curves and can distinguish transit-like signals (planet candidates and eclipsing binaries) from stellar variability and instrumental noise with an average precision of 97.0% and an accuracy of 97.4%. For the vetting version of this model, see Astronet Vetting.
Stella: Convolutional Neural Networks for Flare Identification in TESS 2-minute Data
Feinstein et al. 2020a,b
The purpose of stella is to identify flares in TESS short-cadence data with a convolutional neural network (CNN). In its simplest form, stella takes a pre-trained CNN (available on MAST: https://archive.stsci.edu/hlsp/stella) and a light curve (time, flux, and flux error) and returns a probability light curve. The cadences in the probability light curve are values between 0 and 1, where 1 means the CNN believes there is a flare there. It takes < 1 minute to predict flares on a single light curve. Users also have the ability to train their own customized CNN architecture. The stella software also includes modules to measure rotation periods and fit flares using simple exponential models.
TRICERATOPS: Bayesian Vetting and Validation Tool for Transiting Exoplanet Candidates
Giacalone, S. et al.
TRICERATOPS is a Bayesian vetting and validation tool for TESS planet candidates. For a given planet candidate, the tool calculates the probabilities of several astrophysical transit-producing scenarios using the TESS light curve, information about nearby stars, and follow-up observations (e.g., high-resolution imaging, spectroscopy, and time-series photometry). Using these probabilities, TRICERATOPS calculates a false positive probability (the overall probability of the transit being caused by an astrophysical false positive) and a nearby false positive probability (the probability of the transit being caused by an off-target event around a nearby star).
tpfplotter: TESS Target Pixel File Creator
J. Lillo-Box (Aller et al., 2020, A&A, 635, 128)
tpfplotter is a user-friendly tool to create the TESS Target Pixel Files of your favorite source overplotting the aperture mask used by the SPOC pipeline and the Gaia catalogue to check for possible contaminations within the aperture. Create paper-ready figures (1-column) overplotting the Gaia DR2 catalog to the TESS Target Pixel Files. You can create plots for any target observed by TESS! Even if you do not have a TIC number, you can search by coordinates now (see examples in Github)!
eleanor: Python Package that Extracts TESS Target Pixel Files and Produces Systematics-Corrected Light Curves
Feinstein, A. D., Montet, B. T., Foreman-Mackey, D., et al.
Eleanor is a Python package that extracts target pixel files from TESS Full Frame Images and produces systematics-corrected light curves for any star observed by the TESS mission. In its simplest form, eleanor takes a TIC ID, a Gaia source ID, or (RA, Dec) coordinates of a star observed by TESS and returns, as a single object, a light curve and accompanying target pixel data. Paper: Feinstein et al., eleanor: An open-source tool for extracting light curves from the TESS Full-Frame Images, 2019
Lightkurve: Python Package that Analyzes Astronomical Flux Time Series Data
v2.5.0
Vinícius, Barentsen, Hedges, et al.
The lightkurve Python package offers a beautiful and user-friendly way to analyze astronomical flux time series data, in particular the pixels and lightcurves obtained by NASA’s Kepler, K2, and TESS missions.