2021/02/28 by L. Ofman, Leon Ofman, Amir Averbuch +5 · 1 citation
Computer Science · Engineering · Physics and Astronomy · #Artificial intelligence #Astronomical Observations and Instrumentation #Astronomy #Astronomy and Astrophysical Research #Computer science #Exoplanet #Identification (biology) #Kepler #Light curve #Physics #Planet #Satellite #Stellar, planetary, and galactic studies #Vetting #astro-ph.EP #astro-ph.IM #cs.LG
paper · pdf · doi:10.1016/j.newast.2021.101693
Accepted for publication in New Astronomy
arxiv created 2021/08/27 · openalex publication_date 2021/08/30 · arxiv updated 2021/09/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.