Anomaly detection in Fink. I. Discovery, follow-up, and classification of unusual sources
M. V. Pruzhinskaya, M. V. Kornilov, A. V. Dodin, A. Baluta, T. A. Pshenichniy, A. M. Zubareva, E. E. O. Ishida, J. Peloton, I. Beschastnov, I. Ippolitov, A. A. Belinski, P. Golysheva, N. P. Ikonnikova, V. A. Kiryukhina, V. V. Krushinsky, A. M. Tatarnikov, S. G. Zheltoukhov

TL;DR
This paper presents the first-year results of an anomaly detection pipeline within the Fink broker, demonstrating its effectiveness in discovering rare astrophysical phenomena from ZTF alert streams through automated analysis and expert follow-up.
Contribution
The paper introduces an anomaly detection pipeline that combines machine learning, expert feedback, and rapid follow-up to identify and validate rare astronomical events in real-time.
Findings
Discovered multiple rare sources including an AM CVn system and an UX Ori-type star.
Triggered 33 supernova candidates, 30 of which were previously unreported.
Detected nine new dwarf novae and other unusual transient phenomena.
Abstract
Modern wide-field time-domain surveys produce alert streams whose scientific potential is often concentrated in rare and unusual events. Efficient discovery therefore requires automated pipelines to be combined with rapid expert validation and follow-up. We present the first-year performance of the anomaly-detection (AD) pipeline operating within the Fink broker on the Zwicky Transient Facility alert stream, and assess its ability to identify scientifically valid outliers and enable discovery of rare phenomena. The pipeline transforms ZTF light curves into a compact set of features and ranks alerts using an Isolation Forest model trained on archival ZTF data. Each night, the 10 most anomalous candidates are distributed to experts via Slack/Telegram and exposed through an API. We also implement an expert-feedback loop using a public Telegram bot and retrain the model using the Active…
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