Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

Aleo, P. D.; Engel, A. W.; Narayan, G.; Angus, C. R.; Malanchev, K.; Auchettl, K.; Baldassare, V. F.; Berres, A.; de Boer, T. J. L.; Boyd, B. M.; Chambers, K. C.; Davis, K. W.; Esquivel, N.; Farias, D.; Foley, R. J.; Gagliano, A.; Gall, C.; Gao, H.; Gomez, S.; Grayling, M.; Jones, D. O.; Lin, C. -C.; Magnier, E. A.; Mandel, K. S.; Matheson, T.; Raimundo, S. I.; Shah, V. G.; Soraisam, M. D.; de Soto, K. M.; Vicencio, S.; Villar, V. A.; Wainscoat, R. J.

doi:10.3847/1538-4357/ad6869

Title: Anomaly Detection and Approximate Similarity Searches of Transients in Real-time Data Streams

Journal Article · 10 October 2024 · The Astrophysical Journal

DOI: https://doi.org/10.3847/1538-4357/ad6869 · OSTI ID:2467566

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Abstract We present Lightcurve Anomaly Identification and Similarity Search ( LAISS ), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes ( spectroscopic anomalies), of uncommon host galaxy environments ( contextual anomalies), and of peculiar or interaction-powered phenomena ( behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

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Sponsoring Organization:: USDOE

OSTI ID:: 2467566

Journal Information:: The Astrophysical Journal, Journal Name: The Astrophysical Journal Journal Issue: 2 Vol. 974; ISSN 0004-637X

Publisher:: American Astronomical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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pandas-dev/pandas: Pandas https://doi.org/10.5281/zenodo.3509134	software	January 2024
YSE-PZ: An Open-source Target and Observation Management System Coulter, D. A.; Jones, D. O.; McGill, P. https://doi.org/10.5281/zenodo.7278430	software	November 2022
Gaussian Processes for Machine Learning Rasmussen, Carl Edward; Williams, Christopher K. I. The MIT Press https://doi.org/10.7551/mitpress/3206.001.0001	book	January 2005

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