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

arXiv: https://arxiv.org/abs/2102.10326 · HTML: https://mlsanity.com/paper/2102.10326/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 20 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [Exoplanet Validation with Machine Learning: 50 new validated Kepler planets](https://mlsanity.com/paper/2008.10516/) (2020)

## Built on by (most cited first, 3 of 3)

- [Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image Observations](https://mlsanity.com/paper/2301.01371/) (2023)
- [A Possible Converter to Denoise the Images of Exoplanet Candidates through Machine Learning Techniques](https://mlsanity.com/paper/2301.04292/) (2023)
- [Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network](https://mlsanity.com/paper/2510.17459/) (2025)

## Abstract (arXiv)

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.