Perspective on machine learning for real-time analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb
Year: 2026
Type: article
Abstract: The field of high energy physics (HEPs) has seen a marked increase in the use of machine learning (ML) techniques in recent years. The proliferation of applications has revolutionised many aspects of the data processing pipeline at collider experiments including the Large Hadron Collider (LHC). In this whitepaper, we discuss the increasingly crucial role that ML plays in real-time analysis (RTA) at the LHC, namely in the context of the unique challenges posed by the trigger systems of the large LHC experiments. We describe a small selection of the ML applications in use at the large LHC experiments to demonstrate the breadth of use-cases. We continue by emphasising the importance of collaboration and engagement between the HEP community and industry, highlighting commonalities and synergies between the two. The mutual benefits are showcased in several interdisciplinary examples of RTA from industrial contexts. This whitepaper, compiled by the SMARTHEP network, does not provide an exhaustive review of ML at the LHC but rather offers a high-level overview of specific real-time use cases.
Institutions: Lund University, Centre National de la Recherche Scientifique, Sorbonne Université, Laboratoire de Physique Nucléaire et de Hautes Énergies, Institut National de Physique Nucléaire et de Physique des Particules, Istituto Nazionale di Fisica Nucleare, Sezione di Milano Bicocca, University of Milano-Bicocca, University of Geneva, Heidelberg University, LIP6, University of Helsinki, University of Manchester, Verizon (United States), University of Bologna, TU Dortmund University
Language: English
Subfield: Nuclear and High Energy Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W7119507377