arXiv Machine Learning August 17, 2026 By Haoyi Jia, Sagar Addepalli, Julia Gonski

Contrastive Learning for Interpretable Anomaly Detection at Collider Experiments

Read the original on arXiv Machine Learning →

arXiv:2608. 13652v1 Announce Type: new Abstract: Generic event-level anomaly detection for collider physics has two recurring problems: anomaly scores are hard to interpret, and they correlate strongly with energy scale and object multiplicity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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