A Convolutional Autoencoder-Based Pipeline for Anomaly Detection and Classification of Periodic Variables

TL;DR

This paper introduces a convolutional autoencoder pipeline combined with an isolation forest for automatic anomaly detection and classification of periodic variable stars, enhancing discovery of rare stellar phenomena.

Contribution

It presents a novel semi-supervised method integrating autoencoders and random forests for anomaly detection and classification in stellar variability data.

Findings

Most anomalous stars are highly variable, irregular evolved stars.

Anomaly scores effectively identify rare stellar objects.

Latent features facilitate classification of periodic variables.

Abstract

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 Catalog of Periodic Variable Stars (ZTF CPVS). We use an isolation forest to rank each periodic variable by its anomaly score. Our overall most anomalous events have a unique physical origin: they are mostly highly variable and irregular evolved stars. Multiwavelength data suggest that they are most likely Red Giant or Asymptotic Giant Branch stars concentrated in the Milky Way galactic disk. Furthermore, we show how the learned latent features can be used 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.