Anomaly Hunter for Alerts (AHA): Anomaly Detection in the ZTF Transient Alert Stream
Modern time-domain surveys produce alert streams at a scale that makes exhaustive manual inspection infeasible, requiring automated methods to identify unusual transients for follow-up. In this work, we present an unsupervised anomaly detection pipeline applied to the ZTF alert stream using the Lasair broker. We define normal objects as SN Ia, SN II, and SN Ib/c. Anomalous objects include (i) more exotic transients (AGN, TDEs, SLSNe, CVs, and nuclear transients) and (ii) supernova-labeled objects, either spectroscopically or by Lasair, with anomalous properties, such as incorrect or absent host associations, or non-supernova-like light curves. Our pipeline consists of three independently trained simple autoencoders operating on distinct alert stream data products: object features, triplet image cutouts, and light curves. Each model is trained on predominantly normal transients, and performance is assessed using the recall of exotic objects and the purity of all anomalous objects across both a spectroscopically classified held-out test set and the live alert stream. In the test set, performance is evaluated at a fixed rank corresponding to the top ten scoring candidates, while in the alert stream it is evaluated using an anomaly threshold defined from test set behavior. Across both settings, the algorithms consistently recover exotic transients and anomalous supernovae among their top-ranked candidates. Over 25 days of live alert stream application, we identify 87 unusual supernova candidates for follow-up. The overlap between anomalies flagged by different autoencoders in the test set is non-existent, and in the alert stream is small, with maximum overlap between any two algorithms being 11 objects. The framework is data-efficient, requiring only a few thousand training examples, making it well suited for early and ongoing application to the Rubin Observatory alert stream.