Novel machine learning applications at the LHC
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 28 references that are in the map. First posted on arXiv 30 Sep 2024.
Builds on
9 of 28 references.
References · 9
- 2017Attention Is All You Needcited by 59,932
- 2018Fast inference of deep neural networks in FPGAs for particle physicscited by 132
- 2022Particle Transformer for Jet Taggingcited by 95
- 2021A Living Review of Machine Learning for Particle Physicscited by 77
- 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
- 2019OmniFold: A Method to Simultaneously Unfold All Observablescited by 31
- 2019Neural Networks for Full Phase-space Reweighting and Parameter Tuningcited by 20
- 2023Machine-learning-based particle identification with missing datacited by 3
- 2024Particle identification with machine learning from incomplete data in the ALICE experimentcited by 3
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Citing papers per year: 2026: 1.
1 paper.