Machine Learning in High Energy Physics Community White Paper

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...

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Published in:	Journal of physics. Conference series Vol. 1085; no. 2; pp. 22008 - 22034
Main Authors:	Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Andrews, Michael, Araque Espinosa, Juan Pedro, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bonacorsi, Daniele, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Childers, Taylor, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, Duarte, Javier, Erdmann, Martin, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Hacker, Thomas, Hegner, Benedikt, Heinrich, Lukas, Hooberman, Ben, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, 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, Narain, Meenakshi, Neubauer, Mark, Newman, Harvey, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Sokoloff, Mike, Stewart, Graeme, Stockdale, Ian, Strong, Giles, Thais, Savannah, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vallecorsa, Sofia, Vasel, Justin, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Zapata, Omar
Format:	Journal Article
Language:	English
Published:	Bristol IOP Publishing 01.09.2018 Institute of Physics
Subjects:	Collaboration Computer Science - Machine Learning High Energy Physics - Experiment Industrial Electronics Industriell elektronik Large Hadron Collider Luminosity Machine learning Neutrinos Paper machines Particle physics Physical, chemical, mathematical & earth Sciences Physics Physics - Computational Physics PHYSICS OF ELEMENTARY PARTICLES AND FIELDS Physique Physique, chimie, mathématiques & sciences de la terre R&D Research & development Statistics - Machine Learning
ISSN:	1742-6588, 1742-6596, 1742-6596
Online Access:	Get full text
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 scopus-id:2-s2.0-85055779283 AC02-07CH11359 FERMILAB-PUB-18-318-CD-DI-PPD; arXiv:1807.02876 USDOE Office of Science (SC), High Energy Physics (HEP)
ISSN:	1742-6588 1742-6596 1742-6596
DOI:	10.1088/1742-6596/1085/2/022008