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arXiv · 2506.16314

Signatures to help interpretability of anomalies

Emmanuel Gangler·Emille E. O. Ishida·Matwey V. Kornilov·Vladimir Korolev·Anastasia Lavrukhina·Konstantin Malanchev·Maria V. Pruzhinskaya·Etienne Russeil·Timofey Semenikhin·Sreevarsha Sreejith·Alina A. Volnova
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Abstract

Machine learning is often viewed as a black box when it comes to understanding its output, be it a decision or a score. Automatic anomaly detection is no exception to this rule, and quite often the astronomer is left to independently analyze the data in order to understand why a given event is tagged as an anomaly. We introduce here idea of anomaly signature, whose aim is to help the interpretability of anomalies by highlighting which features contributed to the decision.

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Emmanuel Gangler, Emille E. O. Ishida, Matwey V. Kornilov, Vladimir Korolev, Anastasia Lavrukhina, Konstantin Malanchev, Maria V. Pruzhinskaya, Etienne Russeil, Timofey Semenikhin, Sreevarsha Sreejith, Alina A. Volnova. 2025-06-19. Signatures to help interpretability of anomalies. https://arxiv.org/abs/2506.16314

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