Cited by 417Particle physics theoretical and experimental studiesParticle Detector Development and PerformanceHigh-Energy Particle Collisions Research

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Deep Learning and Its Application to LHC Physics

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Guest, Dan, Cranmer, Kyle, Whiteson, Daniel·2018·Annual Review of Nuclear and Particle Science

DOI 10.1146/annurev-nucl-101917-021019

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Abstract

Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex problems than previously feasible. This review is aimed at the reader who is familiar with high-energy physics but not machine learning. The connections between machine learning and high-energy physics data analysis are explored, followed by an introduction to the core concepts of neural networks, examples of the key results demonstrating the power of deep learning for analysis of LHC data, and discussion of future prospects and concerns.

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Ai Assisted Particle Physics Simulation
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Unraveling Dirac Magnetic Monopoles with Muon Beams at TeV Energies Using Machine Learning
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An interactive machine learning platform for analyzing multi-particle coincidence data from cold target recoil ion momentum spectroscopy
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A Comparative Study of Convolutional Neural Networks for Jet-Image Classification
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“Over the last few years, deep learning has advanced significantly in biology [1], materials, physics [2], information science [3], and many other applications that profoundly changed our daily life.”
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Probing the Frontiers of High-Energy Physics: Deep Learning-Based Invariant Mass Prediction from Electron-Electron Collisions
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Future of computing at the Large Hadron Collider
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Machine learning applied to pattern characterization in spatially extended dynamical systems
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Detection of collinear high energetic di-photon signatures with Micromegas Detectors
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Neutral pion reconstruction using machine learning in the experiment at 〈E ν〉 6 GeV
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Cross-section measurements of top-quark pair production in association with a hard photon at 13 TeV with the ATLAS detector
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Redes Auto-codificadoras como Pré-processamento em Calorimetria de Altas Energias com Fina Segmentação
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Background
Search for the associated production of a single top quark and a Z or Higgs boson with the CMS experiment
Tonon, N. (2019) · doi:10.70675/35444bc3z677cz4130za322z0871fcbf711a
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Particle Identification at STCF DTOF Detector Based on Classical/Quantum Convolutional Neural Network
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Machine Learning in High-Energy Physics: Illustrative Applications in Theory and Experiment
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Predicting Exotic Hadron Masses with Data Augmentation Using Multilayer Perceptron
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Towards Layer-Wise Optimization of Contextual Neural Networks with Constant Field of Aggregation
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The Impact of Aggregation Window Width on Properties of Contextual Neural Networks with Constant Field of Attention
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Data Analysis
Dissertori, G. (2021) · Handbook of Particle Detection and Imaging · doi:10.1007/978-3-319-93785-4_4
Artificial Neural Networks
Czischek, S. (2020) · Springer theses · doi:10.1007/978-3-030-52715-0_3 · Cited by 3
Data Analysis
Dissertori, G. (2019) · Handbook of Particle Detection and Imaging · doi:10.1007/978-3-319-47999-6_4-2

Authors: Guest, D., Cranmer, K., & Whiteson, D.
Affiliations: University of California, Irvine (US)
New York University (US)
ORCID: Guest, Dan
Cranmer, Kyle
Whiteson, Daniel
Venue: Annual Review of Nuclear and Particle Science
Publisher: Annual Reviews
Published: 2018-10-19
Volume: 68
Issue: 1
Pages: 161-181
ISSN: 0163-8998 (ISSN-L)
1545-4134
DOI: 10.1146/annurev-nucl-101917-021019
OpenAlex: W2810437889
Cited by: 417
References: 40
Citations by year: 2026: 28 · 2025: 42 · 2024: 71 · 2023: 43 · 2022: 58 · 2021: 73 · 2020: 60 · 2019: 36 · 2018: 5
FWCI: 3.71
Type: journal article
Language: en
Open access: green
arxiv.org
Locations: Annual Review of Nuclear and Particle Science (publishedVersion)
arXiv (Cornell University) (submittedVersion)
arXiv (Cornell University)
Topics: Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Particle physics theoretical and experimental studies
Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Particle Detector Development and Performance
Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › High-Energy Particle Collisions Research
Keywords: deep learning, high energy physics data analysis, Large Hadron Collider, artificial neural networks, machine learning in particle physics, LHC data analysis

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Deep Learning and Its Application to LHC Physics

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