Machine Learning for Exoplanet Discovery: Validating TESS Candidates and Identifying Planets in the Habitable Zone

TL;DR

This paper presents a machine learning framework that improves the validation of exoplanet candidates from TESS data, discovering new planets including some in the habitable zone, and demonstrating the method's efficiency and adaptability.

Contribution

The study introduces a novel machine learning model trained on Kepler data to identify true exoplanets in TESS candidates, including habitable-zone planets, with high accuracy and discovery potential.

Findings

Identified 1595 new high-confidence exoplanets in TESS data.

Recovered 86% of known TESS exoplanets in validation.

Discovered multiple multi-planet systems, including habitable-zone planets.

Abstract

The high-precision photometry from NASA's Kepler and TESS missions has revolutionized exoplanet detection, enabling the discovery of over 5500 confirmed exoplanets via the transit method and around 10000 additional candidates awaiting validation. However, confirming these candidates as true planets demands meticulous vetting and follow-up observations, which hampers the discovery of exoplanets in large-scale datasets. To address this challenge, we developed a machine learning framework trained on Kepler's catalog of confirmed exoplanets and false positives to accurately identify true planetary candidates. Our model uses transit properties, planetary characteristics, and host stellar parameters as training features. The optimized model achieved 83.9% accuracy in cross-validation. When applied to 3987 TESS candidates with complete observational data, the model identified 1595 new high-confidence planets and correctly recovered 86% (358/418) of all previously confirmed TESS exoplanets in a blinded validation test. Our analysis revealed 100 previously unrecognized multi-planet systems, including five systems--that host habitable-zone exoplanets. Additionally, we identified 15 more planets within the habitable zone of a single system, suggesting strong potential for liquid water stability under conservative planetary albedo assumptions. This work demonstrates that machine learning can accelerate exoplanet validation while maintaining scientific rigor. Our modular design enables direct adaptation to future photometric missions like PLATO or Earth 2.0.