Accès ouvert déclaré
2018
preprint
Machine Learning in High Energy Physics Community White Paper
Kim Albertsson, Piero Altoè, Dustin Anderson, John Anderson, Michael Benjamin Andrews, Juan Pedro Araque Espinosa, A. Aurisano, L. Basara, A. J. Bevan, W. Bhimji, Daniele Bonacorsi, B. Burkle, P. Calafiura, M. Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yifan Chen, J. T. Childers, Y. Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, J. Duarte, Martin Erdmann, Jonas Nathanael Eschle, A. Farbin, M. Feickert, N. F. Castro, C. Fitzpatrick, M. Floris, A. C. Forti, J. Garra-Tico, Jochen Gemmler, Maria Girone, P. C. F. Glaysher, Sergei Gleyzer, V. V. Gligorov, T. Golling, Jonas Graw, L. Gray, Dick Greenwood, Thomas J. Hacker, J. Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, M. Kagan, Meghan Kane, K. Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gautam Kaul, D. Kçira, Thomas M. Keck, A. Klimentov, Jim Kowalkowski, Luke Kreczko, A. Kurepin, Rob Kutschke, В. Е. Кузнецов, N. M. Köhler, I. Lakomov, K. Lannon, M. Lassnig, A. Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, H. Meinhard, D. Menasce, L. Moneta, S. Moortgat, M. S. Neubauer, H. B. Newman, Sydney Otten, Hans Pabst, Michela Paganini, M. Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, J. Pivarski, H. Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, E. Rodrigues, Jamal Rorie, D. Rousseau, Aaron Sauers, S. Schramm, H. Severini, P. Seyfert, Filip Siroký, Konstantin Skazytkin, Mike Sokoloff, G. A. Stewart, Bob Stienen, I. E. Stockdale, G. Strong, W. Sun, S. J. Thais, Karen Tomko, Eli Upfal, E. Usai, A. Ustyuzhanin, Martin Vala, J. Vasel, S. Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, I. Vukotić, Sean-Jiun Wang, G. Watts, Michael Williams, Wenjing Wu, Stefan Wünsch, Kun Yang, Omar Zapata
31Citations signalées, ce qui n’est pas une note de qualité
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Le résumé fourni par la source
Machine learning has been applied to several problems in particle physics research, 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 for machine learning in particle physics. We detail 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.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
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Les sujets associés
Particle physics theoretical and experimental studiesParticle Detector Development and PerformanceNeutrino Physics Research