Machine Learning in High Energy Physics

Abstract:
Machine Learning (ML) algorithms are increasingly being adopted to address a broad variety of challenges in experimental High Energy Physics (HEP), including event classification, clustering, particle identification and more. This talk charts the evolution of ML in HEP, from the deployment of standard image recognition architectures to the development of advanced graph neural network (GNN) and sparse convolution neural network (SCNN) applications. Particular reference is taken to two major particle physics experiments, the Deep Underground Neutrino Experiment (DUNE) and the High-Luminosity Large Hadron Collider (HL-LHC), in order to highlight both their commonalities and the unique challenges posed by the neutrino and collider paradigms. These approaches are then situated within the context of a typical particle physics analysis, which requires robust error estimation and quantification of an algorithm's inefficiency and bias.

 

Bio:
V Hewes is a neutrino physics researcher at the University of Cincinnati, based at the Fermi National Accelerator Laboratory (Fermilab). She is a member of the NOvA collaboration, on which she conducts neutrino oscillation analysis and acts as computing coordinator. She also collaborates on the next-generation DUNE experiment, for which she develops GNN reconstruction techniques for Liquid Argon detectors as part of the ExaTrkX collaboration. Her primary research interests lie at the intersection of experimental physics and scientific computing, developing generalized tools for reconstruction and analysis that can be leveraged across a diverse range of experiments and architectures.

Summary