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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of physics. Conference series Jg. 1085; H. 2; S. 22008 - 22034
Hauptverfasser: 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
Sprache:Englisch
Veröffentlicht: Bristol IOP Publishing 01.09.2018
Institute of Physics
Schlagworte:
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-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
Bibliographie: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