Novel machine learning applications at the LHC

2024/09/30 by J. Duarte, Duarte, Javier M. · 6 citations
Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #High-Energy Particle Collisions Research #Machine Learning (cs.LG) #Particle Detector Development and Performance #Particle physics theoretical and experimental studies

paper · pdf · doi:10.48550/arxiv.2409.20413

openalex publication_date 2024/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Machine learning (ML) is a rapidly growing area of research in the field of particle physics, with a vast array of applications at the CERN LHC. ML has changed the way particle physicists conduct searches and measurements as a versatile tool used to improve existing approaches and enable fundamentally new ones. In these proceedings, we describe novel ML techniques and recent results for improved classification, fast simulation, unfolding, and anomaly detection in LHC experiments.

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