Event-Based Anomaly Detection for Searches for New Physics
S. Chekanov, W. H. Hopkins
Abstract
This paper discusses model-agnostic searches for new physics at the Large Hadron Collider 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 a machine-learning approach based on autoencoders. The analysis uses Monte Carlo simulations for the SM background and several selected exotic models. We also investigate the input space for the event-based anomaly detection and illustrate the shapes of invariant masses in the outlier region which will be used to perform searches for resonant phenomena beyond the SM. Challenges and conceptual limitations of this approach are discussed.
Topics & Concepts
PhysicsAnomaly (physics)Event (particle physics)Particle physicsPhysics beyond the Standard ModelAnomaly detectionNuclear physicsAstrophysicsData miningQuantum mechanicsComputer scienceParticle physics theoretical and experimental studiesDark Matter and Cosmic PhenomenaComputational Physics and Python Applications