Searching for Anomalies in the ZTF Catalog of Periodic Variable Stars

Abstract

Periodic variables illuminate the physical processes of stars throughout their lifetime. Wide-field surveys continue to increase our discovery rates of periodic variable stars. Automated approaches are essential to identify interesting periodic variable stars for multiwavelength and spectroscopic follow-up. Here we present a novel unsupervised machine-learning approach to hunt for anomalous periodic variables using phase-folded light curves presented in the Zwicky Transient Facility Catalogue of Periodic Variable Stars by Chen et al. We use a convolutional variational autoencoder to learn a low-dimensional latent representation, and we search for anomalies within this latent dimension via an isolation forest. We identify anomalies with irregular variability. Most of the top anomalies are likely highly variable red giants or asymptotic giant branch stars concentrated in the Milky Way galactic disk; a fraction of the identified anomalies are more consistent with young stellar objects. Detailed spectroscopic follow-up observations are encouraged to reveal the nature of these anomalies.