Machine Learning in High Energy Physics Community White Paper
Author(s)
Albertsson, Kim; Altoe, Piero; Anderson, Dustin; Andrews, Michael; Araque Espinosa, Juan Pedro; Aurisano, Adam; Basara, Laurent; Bevan, Adrian; Bhimji, Wahid; Bonacorsi, Daniele; Calafiura, Paolo; Campanelli, Mario; Capps, Louis; Carminati, Federico; Carrazza, Stefano; Childers, Taylor; Coniavitis, Elias; Cranmer, Kyle; David, Claire; Davis, Douglas; Duarte, Javier; Erdmann, Martin; Eschle, Jonas; Farbin, Amir; Feickert, Matthew; Castro, Nuno Filipe; Fitzpatrick, Conor; Floris, Michele; Forti, Alessandra; Garra-Tico, Jordi; Gemmler, Jochen; Girone, Maria; Glaysher, Paul; Gleyzer, Sergei; Gligorov, Vladimir; Golling, Tobias; Graw, Jonas; Gray, Lindsey; Greenwood, Dick; Hacker, Thomas; Harvey, John; Hegner, Benedikt; Heinrich, Lukas; Hooberman, Ben; Junggeburth, Johannes; Kagan, Michael; Kane, Meghan; Kanishchev, Konstantin; Karpiński, Przemysław; Kassabov, Zahari; Kaul, Gaurav; Kcira, Dorian; Keck, Thomas; Klimentov, Alexei; Kowalkowski, Jim; Kreczko, Luke; Kurepin, Alexander; Kutschke, Rob; Kuznetsov, Valentin; Köhler, Nicolas; Lakomov, Igor; Lannon, Kevin; Lassnig, Mario; Limosani, Antonio; Louppe, Gilles; Mangu, Aashrita; Mato, Pere; Meinhard, Helge; Menasce, Dario; Moneta, Lorenzo; Moortgat, Seth; Narain, Meenakshi; Neubauer, Mark; Newman, Harvey; Pabst, Hans; Paganini, Michela; Paulini, Manfred; Perdue, Gabriel; Perez, Uzziel; Picazio, Attilio; Pivarski, Jim; Prosper, Harrison; Psihas, Fernanda; Radovic, Alexander; Reece, Ryan; Rinkevicius, Aurelius; Rodrigues, Eduardo; Rorie, Jamal; Rousseau, David; Sauers, Aaron; Schramm, Steven; Schwartzman, Ariel; Severini, Horst; Seyfert, Paul; Siroky, Filip; Skazytkin, Konstantin; Sokoloff, Mike; Stewart, Graeme; Stienen, Bob; Stockdale, Ian; Strong, Giles; Thais, Savannah; Tomko, Karen; Upfal, Eli; Usai, Emanuele; Ustyuzhanin, Andrey; Vala, Martin; Vallecorsa, Sofia; Vasel, Justin; Verzetti, Mauro; Vilasís-Cardona, Xavier; Vlimant, Jean-Roch; Vukotic, Ilija; Wang, Sean-Jiun; Watts, Gordon; Williams, Michael; Wu, Wenjing; Wunsch, Stefan; Zapata, Omar; ... Show more Show less
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© Published under licence by IOP Publishing Ltd. 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 promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.
Date issued
2018-09Department
Massachusetts Institute of Technology. Department of PhysicsJournal
Journal of Physics: Conference Series
Publisher
IOP Publishing
Citation
Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Andrews, Michael, Araque Espinosa, Juan Pedro et al. 2018. "Machine Learning in High Energy Physics Community White Paper." Journal of Physics: Conference Series, 1085 (2).
Version: Final published version
ISSN
1742-6588
1742-6596