Automatically detecting anomalous exoplanet transits

TL;DR

This paper introduces a novel autoencoder-based method for automatically detecting anomalous exoplanet transits, significantly improving outlier detection in complex light curve data and providing new datasets for future research.

Contribution

The paper presents a new architecture using paired variational autoencoders to better identify anomalies in exoplanet transit data, a first in automated anomaly detection for this domain.

Findings

Latent representations improve outlier detection accuracy

Method outperforms traditional approaches on fabricated datasets

First automatic detection of 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.