http://arxiv.org/abs/2111.08679
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.
C. Hönes, B. Miller, A. Heras, et. al.
Wed, 17 Nov 21
4/64
Comments: 12 pages, 4 figures, 4 tables, Accepted at NeurIPS 2021 (Workshop for Machine Learning and the Physical Sciences)