Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning

• URL: http://arxiv.org/abs/2401.08777v1
• Date: Tue, 16 Jan 2024 19:00:20 GMT
• Title: Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
• Authors: Abhijith Gandrakota, Lily Zhang, Aahlad Puli, Kyle Cranmer, Jennifer Ngadiuba, Rajesh Ranganath, and Nhan Tran
• Abstract summary: Anomaly detection is a promising tool for aiding discoveries of new particles or processes in particle physics. We build detection algorithms using representation learning from multiple background types. We demonstrate the benefit of the proposed robust multi-background anomaly detection algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.
• Score: 16.301828198874507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Anomaly, or out-of-distribution, detection is a promising tool for aiding discoveries of new particles or processes in particle physics. In this work, we identify and address two overlooked opportunities to improve anomaly detection for high-energy physics. First, rather than train a generative model on the single most dominant background process, we build detection algorithms using representation learning from multiple background types, thus taking advantage of more information to improve estimation of what is relevant for detection. Second, we generalize decorrelation to the multi-background setting, thus directly enforcing a more complete definition of robustness for anomaly detection. We demonstrate the benefit of the proposed robust multi-background anomaly detection algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.

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