Automatically detecting anomalous exoplanet transits
This enables automatic identification of anomalous exoplanet transits for astronomers, representing a novel application but with incremental methodological improvements.
The paper tackles the problem of detecting anomalous exoplanet transits from complex light curve data by proposing a dual variational autoencoder architecture that estimates latent representations for main transits and residual deviations, showing that these representations significantly improve outlier detection on fabricated datasets and applying it to real data.
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.