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, W.; 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; Kaul, Gautam; Kçira, D.; M. Keck, Thomas; Klimentov, A.; Kowalkowski, Jim; Kreczko, Luke; Kurepin, A.; Kutschke, Rob; Е. Кузнецов, В.; M. Köhler, N.; Lakomov, I.; Lannon, K.; Lassnig, M.; Limosani, A.; Louppe, Gilles; Mangu, Aashrita; Mato, Pere; Meinhard, H.; Menasce, D.; Moneta, L.; Moortgat, S.; Narain, M.; S. Neubauer, M.; B. Newman, H.; Pabst, Hans; Paganini, Michela; Paulini, M.; Perdue, Gabriel; Perez, Uzziel; Picazio, Attilio; Pivarski, J.; Prosper, H.; Psihas, Fernanda; Radovic, Alexander; Reece, Ryan; Rinkevicius, Aurelius; Rodrigues, E.; Rorie, Jamal; Rousseau, D.; Sauers, Aaron; Schramm, S.; Schwartzman, Ariel; Severini, H.; Seyfert, P.; Siroký, Filip; Skazytkin, Konstantin; Sokoloff, Mike; A. Stewart, G.; Stienen, Bob; E. Stockdale, I.; Strong, G.; J. Thais, S.; Tomko, Karen; Upfal, Eli; Usai, E.; Ustyuzhanin, A.; Vala, Martin; Vallecorsa, S.; Vasel, J.; Verzetti, Mauro; Vilasís-Cardona, Xavier; Vlimant, Jean-Roch; Vukotić, I.; Wang, Sean-Jiun; Watts, G.; Williams, Michael; Wu, Wenjing; Wünsch, Stefan; Zapata, Omar

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

Public

Machine Learning in High Energy Physics Community White Paper

Published in Journal of Physics Conference Series • Sep 1, 2018

Authors:

Kim Albertsson

Piero Altoè

Dustin Anderson

Abstract

Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications in particle and event identification and reconstruction in the 2010s. In this document we discuss pr...

Subject

Resource (disambiguation)

Neutrino

Particle physics

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