A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients

arXiv:2403.14742v1 Announce Type: cross
Abstract: Automating real-time anomaly detection is essential for identifying rare transients in the era of large-scale astronomical surveys. Modern survey telescopes are generating tens of thousands of alerts per night, and future telescopes, such as the Vera C. Rubin Observatory, are projected to increase this number dramatically. Currently, most anomaly detection algorithms for astronomical transients rely either on hand-crafted features extracted from light curves or on features generated through unsupervised representation learning, which are then coupled …

abstract alerts anomaly anomaly detection arxiv astro-ph.he astro-ph.im class classifier cs.lg detection future modern per real-time rubin observatory scale survey surveys telescopes type vera