Machine learning at the energy and intensity frontiers of particle physics

Alexander Radovic (), Mike Williams (), David Rousseau, Michael Kagan, Daniele Bonacorsi, Alexander Himmel, Adam Aurisano, Kazuhiro Terao and Taritree Wongjirad
Additional contact information
Alexander Radovic: College of William and Mary
Mike Williams: Massachusetts Institute of Technology
David Rousseau: LAL, Université Paris-Sud, CNRS/IN2P3, Université Paris-Saclay
Michael Kagan: SLAC National Accelerator Laboratory
Daniele Bonacorsi: Università di Bologna
Alexander Himmel: Fermi National Accelerator Laboratory
Adam Aurisano: University of Cincinnati
Kazuhiro Terao: SLAC National Accelerator Laboratory
Taritree Wongjirad: Tufts University

Nature, 2018, vol. 560, issue 7716, 41-48

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.nature.com/articles/s41586-018-0361-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:560:y:2018:i:7716:d:10.1038_s41586-018-0361-2

Ordering information: This journal article can be ordered from
https://www.nature.com/

DOI: 10.1038/s41586-018-0361-2

Access Statistics for this article

Nature is currently edited by Magdalena Skipper

More articles in Nature from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().