Automatically detecting anomalous exoplanet transits

Abstract

Raw light curve data from exoplanet transits is too complex to naively apply traditional outlier detection methods. We propose an architecture which estimates a latent representation of both the main transit and residual deviations with a pair of variational autoencoders. We show, using two fabricated datasets, that our latent representations of anomalous transit residuals are significantly more amenable to outlier detection than raw data or the latent representation of a traditional variational autoencoder. We then apply our method to real exoplanet transit data. Our study is the first which automatically identifies anomalous exoplanet transit light curves. We additionally release three first-of-their-kind datasets to enable further research.

12 pages, 4 figures, 4 tables, Accepted at NeurIPS 2021 (Workshop for Machine Learning and the Physical Sciences)

References in corpus (9)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• The Transiting Exoplanet Survey Satellite
• Limb and gravity-darkening coefficients for the TESS satellite at several metallicities, surface gravities, and microturbulent velocities
• PyTransit: Fast and Easy Exoplanet Transit Modelling in Python
• Searching for Exoplanets Using Artificial Intelligence
• Automatic Classification of Kepler Planetary Transit Candidates
• A Machine Learning Technique to Identify Transit Shaped Signals
• Systematic Serendipity: A Test of Unsupervised Machine Learning as a Method for Anomaly Detection
• Disintegrating Rocky Exoplanets