Robust anomaly detection for particle physics using multi-background representation learning
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 (AD) 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 AD. We demonstrate the benefit of the proposed robust multi-background AD algorithms on a high-dimensional dataset of particle decays at the Large Hadron Collider.
- Sponsoring Organization:
- USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
- OSTI ID:
- 2448123
- Journal Information:
- Machine Learning: Science and Technology, Journal Name: Machine Learning: Science and Technology Journal Issue: 3 Vol. 5; ISSN 2632-2153
- Publisher:
- IOP PublishingCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
Similar Records
Anomaly detection in collider physics via factorized observables
Online anomaly detection for multi‐source VMware using a distributed streaming framework