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

Abstract

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.

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [4];  [8]
  1. College of William and Mary, Williamsburg, VA (United States)
  2. Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
  3. Univ. Paris-Sud, Orsay (France)
  4. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  5. Univ. di Bologna, Bologna (Italy); INFN Sezione di Bologna, Bologna (Italy)
  6. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
  7. Univ. of Cincinnati, Cincinnati, OH (United States)
  8. Tufts Univ., Medford, MA (United States)
Publication Date:
Research Org.:
SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1469751
Alternate Identifier(s):
OSTI ID: 1498560
Report Number(s):
FERMILAB-PUB-18-436-ND
Journal ID: ISSN 0028-0836; PII: 361
Grant/Contract Number:  
AC02-76SF00515; AC02-07CH11359
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Nature (London)
Additional Journal Information:
Journal Volume: 560; Journal Issue: 7716; Journal ID: ISSN 0028-0836
Publisher:
Nature Publishing Group
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Radovic, Alexander, Williams, Mike, Rousseau, David, Kagan, Michael, Bonacorsi, Daniele, Himmel, Alexander, Aurisano, Adam, Terao, Kazuhiro, and Wongjirad, Taritree. Machine learning at the energy and intensity frontiers of particle physics. United States: N. p., 2018. Web. doi:10.1038/s41586-018-0361-2.
Radovic, Alexander, Williams, Mike, Rousseau, David, Kagan, Michael, Bonacorsi, Daniele, Himmel, Alexander, Aurisano, Adam, Terao, Kazuhiro, & Wongjirad, Taritree. Machine learning at the energy and intensity frontiers of particle physics. United States. https://doi.org/10.1038/s41586-018-0361-2
Radovic, Alexander, Williams, Mike, Rousseau, David, Kagan, Michael, Bonacorsi, Daniele, Himmel, Alexander, Aurisano, Adam, Terao, Kazuhiro, and Wongjirad, Taritree. 2018. "Machine learning at the energy and intensity frontiers of particle physics". United States. https://doi.org/10.1038/s41586-018-0361-2. https://www.osti.gov/servlets/purl/1469751.
@article{osti_1469751,
title = {Machine learning at the energy and intensity frontiers of particle physics},
author = {Radovic, Alexander and Williams, Mike and Rousseau, David and Kagan, Michael and Bonacorsi, Daniele and Himmel, Alexander and Aurisano, Adam and Terao, Kazuhiro and Wongjirad, Taritree},
abstractNote = {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.},
doi = {10.1038/s41586-018-0361-2},
url = {https://www.osti.gov/biblio/1469751}, journal = {Nature (London)},
issn = {0028-0836},
number = 7716,
volume = 560,
place = {United States},
year = {2018},
month = {8}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 188 works
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