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

TL;DR

This paper presents a novel AI/ML system trained on Kepler data and applied to TESS data, successfully identifying exoplanet candidates and uncovering new exoplanets through automated classification of light curves.

Contribution

It introduces a combined AI/ML approach for automated exoplanet candidate identification in TESS data, validated with Kepler data and applied at scale.

Findings

Identified approximately 50 potential exoplanet candidates from TESS data.

Discovered three new exoplanet candidates through manual vetting.

Demonstrated the first successful large-scale application of combined AI/ML methods to TESS data.

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.