Novel machine learning applications at the LHC
This paper addresses the problem of improving data analysis and experimental capabilities for particle physicists at the LHC, offering incremental advancements across several application areas.
This paper explores novel machine learning techniques applied to various tasks in particle physics at the CERN LHC, including improved classification, fast simulation, unfolding, and anomaly detection. It highlights how ML has become a versatile tool for enhancing existing approaches and enabling new methodologies in searches and measurements.
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.