# Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2101.10919 · HTML: https://mlsanity.com/paper/2101.10919/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 39 references that are in the map.
- First posted on arXiv 26 Jan 2021.

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

- [Universality of Deep Convolutional Neural Networks](https://mlsanity.com/paper/1805.10769/) (2018)

## Built on by (most cited first, 5 of 5)

- [Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection](https://mlsanity.com/paper/2108.00670/) (2021)
- [Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels](https://mlsanity.com/paper/2211.06903/) (2022)
- [Short-Period Variables in TESS Full-Frame Image Light Curves Identified via Convolutional Neural Networks](https://mlsanity.com/paper/2402.12369/) (2024)
- [NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning](https://mlsanity.com/paper/2405.18278/) (2024)
- [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)

## Abstract (arXiv)

The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~75% of the sky throughout its two year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast data trove for transit signals, we aim to provide an approach that is both computationally efficient and produces highly performant predictions. This approach minimizes the required human search effort. We present a convolutional neural network, which we train to identify planetary transit signals and dismiss false positives. To make a prediction for a given light curve, our network requires no prior transit parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present 181 new planet candidates identified by our network, which pass subsequent human vetting designed to rule out false positives. Our neural network model is additionally provided as open-source code for public use and extension.