Machine Learning in High Energy Physics Community White Paper

Albertsson, Kim; Altoè, Piero; Anderson, Dustin; Benjamin Andrews, Michael; Pedro Araque Espinosa, Juan; Aurisano, A.; Basara, L.; J. Bevan, A.; Bhimji, Wahid; Bonacorsi, Daniele; Calafiura, P.; Campanelli, M.; Capps, Louis; Carminati, Federico; Carrazza, Stefano; T. Childers, J.; Coniavitis, Elias; Cranmer, Kyle; David, Claire; Davis, Douglas; Duarte, J.; Erdmann, M.; Nathanael Eschle, Jonas; Farbin, A.; Feickert, M.; F. Castro, N.; Fitzpatrick, C.; Floris, M.; Forti, Alessandra; Garra-Tico, J.; Gemmler, Jochen; Girone, Maria; C. F. Glaysher, P.; Gleyzer, Sergei; V. Gligorov, V.; Golling, T.; Graw, Jonas; Gray, L.; Greenwood, Dick; J. Hacker, Thomas; Harvey, J.; Hegner, Benedikt; Heinrich, Lukas; Hooberman, Ben; Junggeburth, Johannes; Kagan, M.; Kane, Meghan; Kanishchev, K.; Karpiński, Przemysław; Kassabov, Zahari

doi:10.1088/1742-6596/1085/2/022008

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Machine Learning in High Energy Physics Community White Paper

Shared by NobleBlocks on Sep 1, 2018 • 12:00 AM UTC

Authors:

Kim Albertsson

Piero Altoè

Dustin Anderson

Abstract

Machine learning has been applied to several problems in particle physics\nresearch, beginning with applications to high-level physics analysis in the\n1990s and 2000s, followed by an explosion of applications in particle and event\nidentification and reconstruction in the 2010s. In this document we...

Subject

Resource (disambiguation)

Neutrino

Particle physics

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