# Identifying Potential Exomoon Signals with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2109.10503 · HTML: https://mlsanity.com/paper/2109.10503/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 33 references that are in the map.
- First posted on arXiv 22 Sep 2021.

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

- [Single Transit Detection In Kepler With Machine Learning And Onboard Spacecraft Diagnostics](https://mlsanity.com/paper/2403.03427/) (2024)
- [Photometric Search for Exomoons by using Convolutional Neural Networks](https://mlsanity.com/paper/2111.02293/) (2021)
- [Exploring Long-period Architectures: Four New Planet Candidates from Kepler with Periods >342 days](https://mlsanity.com/paper/2608.23425/) (2026)

## Abstract (arXiv)

Targeted observations of possible exomoon host systems will remain difficult to obtain and time-consuming to analyze in the foreseeable future. As such, time-domain surveys such as Kepler, K2 and TESS will continue to play a critical role as the first step in identifying candidate exomoon systems, which may then be followed-up with premier ground- or space-based telescopes. In this work, we train an ensemble of convolutional neural networks (CNNs) to identify candidate exomoon signals in single-transit events observed by Kepler. Our training set consists of ~27,000 examples of synthetic, planet-only and planet+moon single transits, injected into Kepler light curves. We achieve up to 88% classification accuracy with individual CNN architectures and 97% precision in identifying the moons in the validation set when the CNN ensemble is in total agreement. We then apply the CNN ensemble to light curves from 1880 Kepler Objects of Interest with periods >10 days (~57,000 individual transits), and further test the accuracy of the CNN classifier by injecting planet transits into each light curve, thus quantifying the extent to which residual stellar activity may result in false positive classifications. We find a small fraction of these transits contain moon-like signals, though we caution against strong inferences of the exomoon occurrence rate from this result. We conclude by discussing some ongoing challenges to utilizing neural networks for the exomoon search.