Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods. (arXiv:2102.10326v2 [astro-ph.EP] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Ofman_L/0/1/0/all/0/1">Leon Ofman</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Averbuch_A/0/1/0/all/0/1">Amir Averbuch</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Shliselberg_A/0/1/0/all/0/1">Adi Shliselberg</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Benaun_I/0/1/0/all/0/1">Idan Benaun</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Segev_D/0/1/0/all/0/1">David Segev</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Rissman_A/0/1/0/all/0/1">Aron Rissman</a>
A novel artificial intelligence (AI) technique that uses machine learning
(ML) methodologies combines several algorithms, which were developed by
ThetaRay, Inc., is applied to NASA’s Transiting Exoplanets Survey Satellite
(TESS) dataset to identify exoplanetary candidates. The AI/ML ThetaRay system
is trained initially with Kepler exoplanetary data and validated with confirmed
exoplanets before its application to TESS data. Existing and new features of
the data, based on various observational parameters, are constructed and used
in the AI/ML analysis by employing semi-supervised and unsupervised machine
learning techniques. By the application of ThetaRay system to 10,803 light
curves of threshold crossing events (TCEs) produced by the TESS mission,
obtained from the Mikulski Archive for Space Telescopes, the algorithm yields
about 50 targets for further analysis, and we uncover three new exoplanetary
candidates by further manual vetting. This study demonstrates for the first
time the successful application of the particular combined multiple AI/ML-based
methodologies to a large astrophysical dataset for rapid automated
classification of TCEs.
A novel artificial intelligence (AI) technique that uses machine learning
(ML) methodologies combines several algorithms, which were developed by
ThetaRay, Inc., is applied to NASA’s Transiting Exoplanets Survey Satellite
(TESS) dataset to identify exoplanetary candidates. The AI/ML ThetaRay system
is trained initially with Kepler exoplanetary data and validated with confirmed
exoplanets before its application to TESS data. Existing and new features of
the data, based on various observational parameters, are constructed and used
in the AI/ML analysis by employing semi-supervised and unsupervised machine
learning techniques. By the application of ThetaRay system to 10,803 light
curves of threshold crossing events (TCEs) produced by the TESS mission,
obtained from the Mikulski Archive for Space Telescopes, the algorithm yields
about 50 targets for further analysis, and we uncover three new exoplanetary
candidates by further manual vetting. This study demonstrates for the first
time the successful application of the particular combined multiple AI/ML-based
methodologies to a large astrophysical dataset for rapid automated
classification of TCEs.
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