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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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Signatures to Help Interpretability of Anomalies

  • 会议论文

  • 发表日期：

    2026年

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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

摘要

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.