# Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels

arXiv: https://arxiv.org/abs/2211.06903 · HTML: https://mlsanity.com/paper/2211.06903/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 32 references that are in the map.
- First posted on arXiv 13 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning](https://mlsanity.com/paper/1905.07424/) (2019)
- [ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier that Validates 301 New Exoplanets](https://mlsanity.com/paper/2111.10009/) (2021)
- [Exoplanet Validation with Machine Learning: 50 new validated Kepler planets](https://mlsanity.com/paper/2008.10516/) (2020)
- [Identifying Planetary Transit Candidates in TESS Full-Frame Image Light Curves via Convolutional Neural Networks](https://mlsanity.com/paper/2101.10919/) (2021)
- [Identify Light-Curve Signals with Deep Learning Based Object Detection Algorithm. I. Transit Detection](https://mlsanity.com/paper/2108.00670/) (2021)
- [Exoplanet Detection using Machine Learning](https://mlsanity.com/paper/2011.14135/) (2020)

## Built on by (most cited first, 2 of 2)

- [The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits](https://mlsanity.com/paper/2312.02063/) (2023)
- [NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning](https://mlsanity.com/paper/2405.18278/) (2024)

## Abstract (arXiv)

Automated planetary transit detection has become vital to prioritize candidates for expert analysis given the scale of modern telescopic surveys. While current methods for short-period exoplanet detection work effectively due to periodicity in the light curves, there lacks a robust approach for detecting single-transit events. However, volunteer-labelled transits recently collected by the Planet Hunters TESS (PHT) project now provide an unprecedented opportunity to investigate a data-driven approach to long-period exoplanet detection. In this work, we train a 1-D convolutional neural network to classify planetary transits using PHT volunteer scores as training data. We find using volunteer scores significantly improves performance over synthetic data, and enables the recovery of known planets at a precision and rate matching that of the volunteers. Importantly, the model also recovers transits found by volunteers but missed by current automated methods.