ExoMiner

Introduction

This project's mission is to develop, test, and deploy automated machine learning-based methods to sift ('mine') through transit photometry data from exoplanet survey missions such as Kepler and TESS and inform subject matter experts (SMEs) on potential transiting planet candidates.

ExoMiner is trained to classify a given TCE example as an exoplanet or a non-planet (i.e., false positive such as eclipsing binary, background objects, instrumental noise like momentum dumps) based on its prediction score between zero and one and a classification threshold value.

Current main goals

The main goals of the ExoMiner pipeline are:

1. Perform classification of transit signals for TESS (2-min cadence and FFI).
2. Create vetting catalogs based on results produced for TESS SPOC Threshold Crossing Events (TCEs).
3. Validate new exoplanets from TESS.

Model Architectures

• ExoMiner++ (TESS, January 2025) CURRENT
• ExoMiner v1.2 (Kepler Multiplicity Boost paper, June 2023)
• ExoMiner (Kepler paper, February 2022)

Data

All data used in this project are publicly available. Generally, the data consist of:

• TCE and Objects of Interest (e.g. KOI and TOI catalogs) tables available in archives/respositories such as NExSci, ExoFOP, TEV and MAST;
• Light curve and target pixel FITS files and other data products generated by the TESS Science Processing Operations Center (SPOC) pipeline publicly available in archives such as the MAST.

References

For more detailed information see the following publications:

• ExoMiner TESS 2-min paper, arXiv, under review for AJ
• ExoMiner Kepler w/ Multiplicity Boost, published 2023 June 26 in Astronomical Journal, Volume 166, Number 1
  • List of validated exoplanets: link
• ExoMiner Kepler paper, published 2022 February 17 in Astrophysical Journal, Volume 926, Number 2.
  • List of validated exoplanets: link

Contacts

• Hamed Valizadegan (PI, USRA contractor), [email protected]
• Miguel Martinho (Co-I, USRA contractor), [email protected]
• Nikunj Oza (Group Lead, NASA ARC civil servant), [email protected]

Credits

This work was developed by members of the Data Sciences Group, DASH, Intelligent Systems Division (Code-TI) at NASA Ames Research Center (NASA ARC).

• Main Contributors

  • Hamed Valizadegan^1,2, [email protected]
  • Miguel Martinho^1,2, [email protected]

• Collaborators

  • Jon Jenkins¹
  • Doug Caldwell^1,3
  • Joseph Twicken^1,3

1 - NASA Ames Research Center (NASA ARC)
2 - Universities Space Research Association (USRA)
3 - The SETI Institute

Acknowledgements

We would like to acknowledge people that in some way supported our efforts:

• We would like to thank to Steve Bryson and Jeff Smith for their domain expertise.

• David Armstrong for an insightful discussion that improved our work.

• Megan Ansdell for providing information on their code and work.

• Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center.

• We would like to thank all interns that collaborated directly or indirectly to this work: Ashley Raigosa, Andrés Carranza, Fellipe Marcellino, Jennifer Andersson, Kaylie Hausknecht, Laurent Wilkens, Martin Koeling,  Nikash Walia, Noa Lubin, Pedro Gerum, Patrick Maynard, Sam Donald, Theng Yang, Hongbo Wei,  Stuti Agarwal, Joshua Belofsky, Charles Yates, William Zhong, Saiswaroop Thammineni, Kunal Malhotra,  Eric Liang, Ujjawal Prasad, Adithya Giri, Josue Ochoa.

• Miguel Martinho and Hamed Valizadefan are supported through TESS XRP 2022 contract 22-XRP22_2-0173, NASA Academic Services Mission (NAMS) contract number NNA16BD14C as well as the Intelligent Systems Research and Development-3 (ISRDS-3) Contract 80ARC020D00100. Douglas Caldwell and Joseph Twicken are supported through NASA Cooperative Agreement 80NSSC21M0079. 

• We would like to thank multiple people who directly or indirectly contributed to this work. This work includes data collected by the TESS mission and obtained from the MAST data archive at the Space Telescope Science Institute (STScI). Funding for the TESS mission was provided by the NASA Science Mission Directorate. Resources supporting this work were provided by the NASA High-End Computing (HEC) Program through the NASA Advanced Supercomputing (NAS) Division at Ames Research Center for the production of the TESS SOC data products and for training our deep learning model, ExoMiner.  Funding for the TESS mission is provided by NASA's Science Mission Directorate. We acknowledge support from the TESS mission via subaward s3449 from MIT. This work makes use of observations from the LCOGT network. Part of the LCOGT telescope time was granted by NOIRLab through the Mid-Scale Innovations Program (MSIP). MSIP is funded by NSF. This work is based on observations made with the Las Cumbres Observatory’s education network telescopes that were upgraded through generous support from the Gordon and Betty Moore Foundation. This work is based on observations made with the MuSCAT3/4 instruments, developed by the Astrobiology Center (ABC) in Japan, the University of Tokyo, and Las Cumbres Observatory (LCOGT). MuSCAT3 was developed with financial support by JSPS KAKENHI (JP18H05439) and JST PRESTO (JPMJPR1775), and is located at the Faulkes Telescope North on Maui, HI (USA), operated by LCOGT. MuSCAT4 was developed with financial support provided by the Heising-Simons Foundation (grant 2022-3611), JST grant number JPMJCR1761, and the ABC in Japan, and is located at the Faulkes Telescope South at Siding Spring Observatory (Australia), operated by LCOGT. This research has made use of the Exoplanet Follow-up Observation Program (ExoFOP; DOI: 10.26134/ExoFOP5) website, which is operated by the California Institute of Technology, under contract with the National Aeronautics and Space Administration under the Exoplanet Exploration Program.

This material is based upon work supported by the NASA under Agreement No. 80NSSC21K0593 for the program "Alien Earths''. The results reported herein benefitted from collaborations and/or information exchange within NASA’s Nexus for Exoplanet System Science (NExSS) research coordination network sponsored by NASA’s Science Mission Directorate.

This work makes use of observations from the ASTEP telescope. ASTEP benefited from the support of the French and I talian polar agencies IPEV and PNRA in the framework of the Concordia station program and from OCA, INSU, Idex UCAJEDI (ANR- 15-IDEX-01) and ESA through the Science Faculty of the European Space Research and Technology Centre (ESTEC). This research also received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (grant agreement No. 803193/BEBOP) and from the Science and Technology Facilities Council (STFC; grant No. ST/S00193X/1).

See the NASA Open Source Agreement (NOSA) for this software release.

For external collaborators, see the Individual and Corporate Contributor License Agreements.

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