Review of Machine Learning for Real-Time Analysis at the Large Hadron Collider experiments ALICE, ATLAS, CMS and LHCb

TL;DR

This paper reviews the growing role of machine learning in real-time data analysis at the Large Hadron Collider, highlighting applications, challenges, and industry collaborations to enhance experimental efficiency.

Contribution

It provides a high-level overview of specific ML applications in LHC real-time analysis and emphasizes the importance of cross-sector collaboration.

Findings

ML techniques are increasingly used in LHC real-time analysis.

Collaboration between HEP and industry benefits both fields.

Various ML applications demonstrate broad use-cases at LHC.

Abstract

The field of high energy physics (HEP) 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.