Discovering Long-period Exoplanets using Deep Learning with Citizen Science Labels
This addresses the challenge of identifying single-transit events in astronomical surveys for researchers, though it is incremental as it builds on existing deep learning and citizen science approaches.
The paper tackles the problem of detecting long-period exoplanets, which lack robust automated methods, by training a 1-D convolutional neural network using volunteer-labelled transits from the Planet Hunters TESS project, resulting in performance that matches volunteer precision and recovers transits missed by existing automated methods.
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.