A Convolutional Autoencoder-Based Pipeline For Anomaly Detection And Classification Of Periodic Variables

The periodic pulsations of stars teach us about their underlying physical process. We present a convolutional autoencoder-based pipeline as an automatic approach to search for out-of-distribution anomalous periodic variables within the Zwicky Transient Facility (ZTF) catalog of periodic variables. We use an isolation forest to rank each periodic variable by the anomaly score. Our overall most anomalous events have a unique physical origin: they are mostly, red, cool, high variability, and irregularly oscillating periodic variables. Observational data suggest that they are most likely young and massive ($\simeq5-10$M$_\odot$) Red Giant or Asymptotic Giant Branch stars. Furthermore, we use the learned latent feature for the classification of periodic variables through a hierarchical random forest. This novel semi-supervised approach allows astronomers to identify the most anomalous events within a given physical class, significantly increasing the potential for scientific discovery.