# Exoplanet Validation with Machine Learning: 50 new validated Kepler planets

arXiv: https://arxiv.org/abs/2008.10516 · HTML: https://mlsanity.com/paper/2008.10516/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2020.

## Built on by (most cited first, 13 of 13)

- [ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier that Validates 301 New Exoplanets](https://mlsanity.com/paper/2111.10009/) (2021)
- [Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods](https://mlsanity.com/paper/2102.10326/) (2021)
- [Multiplicity Boost Of Transit Signal Classifiers: Validation of 69 New Exoplanets Using The Multiplicity Boost of ExoMiner](https://mlsanity.com/paper/2305.02470/) (2023)
- [Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels](https://mlsanity.com/paper/2211.06903/) (2022)
- [Single Transit Detection In Kepler With Machine Learning And Onboard Spacecraft Diagnostics](https://mlsanity.com/paper/2403.03427/) (2024)
- [The TESS Ten Thousand Catalog: 10,001 uniformly-vetted and -validated Eclipsing Binary Stars detected in Full-Frame Image data by machine learning and analyzed by citizen scientists](https://mlsanity.com/paper/2506.05631/) (2025)
- [ExoMiner++: Enhanced Transit Classification and a New Vetting Catalog for 2-Minute TESS Data](https://mlsanity.com/paper/2502.09790/) (2025)
- [ExoSGAN and ExoACGAN: Exoplanet Detection using Adversarial Training Algorithms](https://mlsanity.com/paper/2207.09665/) (2022)
- [Large-step neural network for learning the symplectic evolution from partitioned data](https://mlsanity.com/paper/2208.14148/) (2022)
- [Exoplanet Detection Using Machine Learning Models Trained on Synthetic Light Curves](https://mlsanity.com/paper/2507.19520/) (2025)
- [ExoNet: Calibrated Multimodal Deep Learning for TESS Exoplanet Candidate Vetting using Phase-Folded Light Curves, Stellar Parameters, and Multi-Head Attention](https://mlsanity.com/paper/2604.15560/) (2026)
- [Machine Learning and Deep Learning for Exoplanet Detection and Atmospheric Characterization with JWST and the Upcoming Ariel Mission](https://mlsanity.com/paper/2606.23766/) (2026)
- [Exploring Long-period Architectures: Four New Planet Candidates from Kepler with Periods >342 days](https://mlsanity.com/paper/2608.23425/) (2026)

## Abstract (arXiv)

Over 30% of the ~4000 known exoplanets to date have been discovered using 'validation', where the statistical likelihood of a transit arising from a false positive (FP), non-planetary scenario is calculated. For the large majority of these validated planets calculations were performed using the vespa algorithm (Morton et al. 2016). Regardless of the strengths and weaknesses of vespa, it is highly desirable for the catalogue of known planets not to be dependent on a single method. We demonstrate the use of machine learning algorithms, specifically a gaussian process classifier (GPC) reinforced by other models, to perform probabilistic planet validation incorporating prior probabilities for possible FP scenarios. The GPC can attain a mean log-loss per sample of 0.54 when separating confirmed planets from FPs in the Kepler threshold crossing event (TCE) catalogue. Our models can validate thousands of unseen candidates in seconds once applicable vetting metrics are calculated, and can be adapted to work with the active TESS mission, where the large number of observed targets necessitates the use of automated algorithms. We discuss the limitations and caveats of this methodology, and after accounting for possible failure modes newly validate 50 Kepler candidates as planets, sanity checking the validations by confirming them with vespa using up to date stellar information. Concerning discrepancies with vespa arise for many other candidates, which typically resolve in favour of our models. Given such issues, we caution against using single-method planet validation with either method until the discrepancies are fully understood.