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

TL;DR

This paper presents a deep learning approach trained on citizen science labels to detect long-period exoplanets from single-transit events, outperforming synthetic data methods and matching volunteer performance.

Contribution

The study introduces a CNN trained on volunteer-labelled data for long-period exoplanet detection, addressing the lack of robust methods for single-transit events.

Findings

Volunteer scores improve detection performance

Model recovers known planets with high precision

Model finds transits missed by existing automated methods

Abstract

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.