Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning
arXiv:2401.08777
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.
References in corpus (6)
- Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC
- Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC
- Equality of Opportunity in Supervised Learning
- NICE: Non-linear Independent Components Estimation
- Measurement of the Positive Muon Anomalous Magnetic Moment to 0.20 ppm
- Autoencoders for Semivisible Jet Detection