Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods

Leon Ofman, Amir Averbuch, Adi Shliselberg, Idan Benaun, David Segev, Aron Rissman

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number101693
JournalNew Astronomy
Volume91
DOIs
StatePublished - Feb 2022

Keywords

  • Computational methods
  • Exoplanet detection methods
  • Machine learning
  • Transit photometry

Fingerprint

Dive into the research topics of 'Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods'. Together they form a unique fingerprint.

Cite this