Reference : Machine Learning in High Energy Physics Community White Paper
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Physics
http://hdl.handle.net/2268/226475
Machine Learning in High Energy Physics Community White Paper
English
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 [> >]
Filipe Castro, Nuno [> >]
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 mailto [Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Big Data >]
Mangu, Aashrita [> >]
Mato, Pere [> >]
Meenakshi, Narain [> >]
Meinhard, Helge [> >]
Menasce, Dario [> >]
Moneta, Lorenzo [> >]
Moortgat, Seth [> >]
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 [> >]
Verzetti, Mauro [> >]
Vilasís-Cardona, Xavier [> >]
Vlimant, Jean-Roch [> >]
Vukotic, Ilija [> >]
Wang, Sean-Jiun [> >]
Watts, Gordon [> >]
Williams, Michael [> >]
Wu, Wenjing [> >]
Wunsch, Stefan [> >]
Zapata, Omar [> >]
8-Jul-2018
Journal of Physics. Conference Series
Institute of Physics
1085
Yes (verified by ORBi)
International
1742-6588
1742-6596
United Kingdom
[en] Physics - Computational Physics ; Computer Science - Machine Learning ; High Energy Physics - Experiment ; Statistics - Machine Learning
[en] Machine learning is an important 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.
Researchers ; Students
http://hdl.handle.net/2268/226475
https://arxiv.org/abs/1807.02876
Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm

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