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:
-
- 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)
- 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:
- Accepted Manuscript
- Journal Name:
- Nature (London)
- Additional Journal Information:
- Journal Name: Nature (London); 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. Wed .
"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},
journal = {Nature (London)},
number = 7716,
volume = 560,
place = {United States},
year = {Wed Aug 01 00:00:00 EDT 2018},
month = {Wed Aug 01 00:00:00 EDT 2018}
}
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