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Perspective on machine learning for real-time analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb

Astrand, S. LU ; Boggia, L. ; Borsato, M. ; Bozianu, L. ; Cocha Toapaxi, C. E. ; Giasemis, F. I. ; Hansen, J. LU orcid ; Inkaew, P. ; Iversen, K. E. LU orcid and Jawahar, P. , et al. (2026) In Machine Learning: Science and Technology 7(1).
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
LHC experiments, machine learning, real-time analysis
in
Machine Learning: Science and Technology
volume
7
issue
1
article number
013001
publisher
IOP Publishing
external identifiers
  • scopus:105028292015
ISSN
2632-2153
DOI
10.1088/2632-2153/ae35cc
language
English
LU publication?
yes
id
45bb49ac-ae63-481e-876b-f4c1a5805d69
date added to LUP
2026-02-19 10:47:01
date last changed
2026-02-19 10:47:08
@article{45bb49ac-ae63-481e-876b-f4c1a5805d69,
  abstract     = {{<p>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.</p>}},
  author       = {{Astrand, S. and Boggia, L. and Borsato, M. and Bozianu, L. and Cocha Toapaxi, C. E. and Giasemis, F. I. and Hansen, J. and Inkaew, P. and Iversen, K. E. and Jawahar, P. and Pineiro Monteagudo, H. and Olocco, M. and Schramm, S.}},
  issn         = {{2632-2153}},
  keywords     = {{LHC experiments; machine learning; real-time analysis}},
  language     = {{eng}},
  number       = {{1}},
  publisher    = {{IOP Publishing}},
  series       = {{Machine Learning: Science and Technology}},
  title        = {{Perspective on machine learning for real-time analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb}},
  url          = {{http://dx.doi.org/10.1088/2632-2153/ae35cc}},
  doi          = {{10.1088/2632-2153/ae35cc}},
  volume       = {{7}},
  year         = {{2026}},
}