Event-based anomaly detection for new physics searches at the LHC using machine learning
- arXiv
- 2111.12119
- Date
- 2021-11-23
- Frontier
- Energy Frontier (EF)
- Topical groups
- Submitted by
- S.V.Chekanov
Abstract
This paper discusses model-agnostic searches for new physics at the Large Hadron Collider (LHC) using anomaly-detection techniques for the identification of event signatures that deviate from the Standard Model (SM). We investigate anomaly detection in the context of machine-learning approaches using autoencoders, and illustrate expected shapes of invariant masses in the outlier region using Monte Carlo simulations. Challenges and conceptual limitations of this approach are discussed.