Event-based anomaly detection for new physics searches at the LHC using machine learning

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.

arXiv comment13 pages, 6 images, contribution to Snowmass 2022

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• Neural Embedding: Learning the Embedding of the Manifold of Physics Data
• Enhancing the hunt for new phenomena in dijet final-states using anomaly detection filters at the High-Luminosity Large Hadron Collider
• Non-resonant Anomaly Detection with Background Extrapolation
• ADFilter -- A Web Tool for New Physics Searches With Autoencoder-Based Anomaly Detection Using Deep Unsupervised Neural Networks