Machine learning at the energy and intensity frontiers of particle physics

Radovic, Alexander; Williams, Mike; Rousseau, David; Kagan, Michael; Bonacorsi, Daniele; Himmel, Alexander; Aurisano, Adam; Terao, Kazuhiro; Wongjirad, Taritree

doi:10.1038/s41586-018-0361-2

Title: Machine learning at the energy and intensity frontiers of particle physics

Journal Article · Wed Aug 01 00:00:00 EDT 2018 · Nature (London)

DOI:https://doi.org/10.1038/s41586-018-0361-2· OSTI ID:1469751

Radovic, Alexander ^[1]; Williams, Mike ^[2]; Rousseau, David ^[3]; Kagan, Michael ^[4]; Bonacorsi, Daniele ^[5]; Himmel, Alexander ^[6]; Aurisano, Adam ^[7]; Terao, Kazuhiro ^[4]; Wongjirad, Taritree ^[8]

College of William and Mary, Williamsburg, VA (United States)
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
Univ. Paris-Sud, Orsay (France)
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Univ. di Bologna, Bologna (Italy); INFN Sezione di Bologna, Bologna (Italy)
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Univ. of Cincinnati, Cincinnati, OH (United States)
Tufts Univ., Medford, MA (United States)

Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics.

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Research Organization:: SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), High Energy Physics (HEP)

Grant/Contract Number:: AC02-76SF00515; AC02-07CH11359

OSTI ID:: 1469751

Alternate ID(s):: OSTI ID: 1498560

Report Number(s):: FERMILAB-PUB-18-436-ND; PII: 361

Journal Information:: Nature (London), Vol. 560, Issue 7716; ISSN 0028-0836

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 203 works

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Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction Belthangady, Chinmay; Royer, Loic A. Nature Methods, Vol. 16, Issue 12 https://doi.org/10.1038/s41592-019-0458-z	journal	July 2019
Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data Maros, Máté E.; Capper, David; Jones, David T. W. Nature Protocols, Vol. 15, Issue 2 https://doi.org/10.1038/s41596-019-0251-6	journal	January 2020
Deep Neural Network Inverse Design of Integrated Photonic Power Splitters Tahersima, Mohammad H.; Kojima, Keisuke; Koike-Akino, Toshiaki Scientific Reports, Vol. 9, Issue 1 https://doi.org/10.1038/s41598-018-37952-2	journal	February 2019
Deep learning for mining protein data Shi, Qiang; Chen, Weiya; Huang, Siqi Briefings in Bioinformatics https://doi.org/10.1093/bib/bbz156	journal	December 2019
Accelerating lattice quantum Monte Carlo simulations using artificial neural networks: Application to the Holstein model Li, Shaozhi; Dee, Philip M.; Khatami, Ehsan Physical Review B, Vol. 100, Issue 2 https://doi.org/10.1103/physrevb.100.020302	journal	July 2019
Context-enriched identification of particles with a convolutional network for neutrino events Psihas, F.; Niner, E.; Groh, M. Physical Review D, Vol. 100, Issue 7 https://doi.org/10.1103/physrevd.100.073005	journal	October 2019
Neural hierarchical models of ecological populations Joseph, Maxwell B. Ecology Letters, Vol. 23, Issue 4 https://doi.org/10.1111/ele.13462	journal	April 2020
Learning representations of irregular particle-detector geometry with distance-weighted graph networks Qasim, Shah Rukh; Kieseler, Jan; Iiyama, Yutaro The European Physical Journal C, Vol. 79, Issue 7 https://doi.org/10.1140/epjc/s10052-019-7113-9	journal	July 2019
Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks Parsons, R. D.; Ohm, S. The European Physical Journal C, Vol. 80, Issue 5 https://doi.org/10.1140/epjc/s10052-020-7953-3	journal	May 2020
Pileup mitigation at the Large Hadron Collider with graph neural networks Arjona Martínez, J.; Cerri, O.; Spiropulu, M. The European Physical Journal Plus, Vol. 134, Issue 7 https://doi.org/10.1140/epjp/i2019-12710-3	journal	July 2019
Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks Parsons, Robert Daniel; Ohm, S. Deutsches Elektronen-Synchrotron, DESY, Hamburg https://doi.org/10.3204/pubdb-2020-02146	text	January 2020
Topology classification with deep learning to improve real-time event selection at the LHC Nguyen, Thong Q.; Weitekamp, Daniel; Anderson, Dustin arXiv https://doi.org/10.48550/arxiv.1807.00083	text	January 2018
Quantum optical neural networks Steinbrecher, Gregory R.; Olson, Jonathan P.; Englund, Dirk arXiv https://doi.org/10.48550/arxiv.1808.10047	preprint	January 2018
Energy Flow Networks: Deep Sets for Particle Jets Komiske, Patrick T.; Metodiev, Eric M.; Thaler, Jesse arXiv https://doi.org/10.48550/arxiv.1810.05165	text	January 2018
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks Martinez, Jesus Arjona; Cerri, Olmo; Pierini, Maurizio arXiv https://doi.org/10.48550/arxiv.1810.07988	preprint	January 2018
Background Rejection in Atmospheric Cherenkov Telescopes using Recurrent Convolutional Neural Networks Parsons, R. D.; Ohm, S. arXiv https://doi.org/10.48550/arxiv.1910.09435	text	January 2019

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