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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…
Jets and Jet Substructure at Future Colliders
Ben Nachman, Salvatore Rappoccio, Nhan Tran +24
Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics p…
Better Latent Spaces for Better Autoencoders
Barry M. Dillon, Tilman Plehn, Christof Sauer +1
Autoencoders as tools behind anomaly searches at the LHC have the structural problem that they only work in one direction, extracting jets with higher complexity but not the other…
A comparison of optimisation algorithms for high-dimensional particle and astrophysics applications
The DarkMachines High Dimensional Sampling Group, Csaba Balázs, Melissa van Beekveld +18
Optimisation problems are ubiquitous in particle and astrophysics, and involve locating the optimum of a complicated function of many parameters that may be computationally expensi…
Learning the latent structure of collider events
Barry M. Dillon, Darius A. Faroughy, Jernej F. Kamenik +1
We describe a technique to learn the underlying structure of collider events directly from the data, without having a particular theoretical model in mind. It allows to infer aspec…