4 papers · 1 filter
Anomaly detection with spiking neural networks for LHC physics
Barry M. Dillon, Jim Harkin, Aqib Javed
Anomaly detection offers a promising strategy for discovering new physics at the Large Hadron Collider (LHC). This paper investigates AutoEncoders built using neuromorphic Spiking…
Theory-informed neural networks for particle physics
Barry M. Dillon, Michael Spannowsky
We present a theory-informed reinforcement-learning framework that recasts the combinatorial assignment of final-state particles in hadron collider events as a Markov decision proc…
Modern Machine Learning for LHC Physicists
Tilman Plehn, Anja Butter, Barry Dillon +3
Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do…
Anomalies, Representations, and Self-Supervision
Barry M. Dillon, Luigi Favaro, Friedrich Feiden +2
We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The AnomalyCLR tech…