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.

Machine Learning in theoretical physics / K. Albertsson, P. Altoe, D. Anderson, M. Andrews, J.P. Araque Espinosa, A. Aurisano, L. Basara, A. Bevan, W. Bhimji, D. Bonacorsi, P. Calafiura, M. Campanelli, L. Capps, F. Carminati, S. Carrazza, T. Childers, E. Coniavitis, K. Cranmer, C. David, D. Davis, J. Duarte, M. Erdmann, J. Eschle, A. Farbin, M. Feickert, N.F. Castro, C. Fitzpatrick, M. Floris, A. Forti, J. Garra-Tico, J. Gemmler, M. Girone, P. Glaysher, S. Gleyzer, V. Gligorov, T. Golling, J. Graw, L. Gray, D. Greenwood, T. Hacker, J. Harvey, B. Hegner, L. Heinrich, B. Hooberman, J. Junggeburth, M. Kagan, M. Kane, K. Kanishchev, P. Karpiński, Z. Kassabov, G. Kaul, D. Kcira, T. Keck, A. Klimentov, J. Kowalkowski, L. Kreczko, A. Kurepin, R. Kutschke, V. Kuznetsov, N. Köhler, I. Lakomov, K. Lannon, M. Lassnig, A. Limosani, G. Louppe, A. Mangu, P. Mato, H. Meinhard, D. Menasce, L. Moneta, S. Moortgat, M. Narain, M. Neubauer, H. Newman, H. Pabst, M. Paganini, M. Paulini, G. Perdue, U. Perez, A. Picazio, J. Pivarski, H. Prosper, F. Psihas, A. Radovic, R. Reece, A. Rinkevicius, E. Rodrigues, J. Rorie, D. Rousseau, A. Sauers, S. Schramm, A. Schwartzman, H. Severini, P. Seyfert, F. Siroky, K. Skazytkin, M. Sokoloff, G. Stewart, B. Stienen, I. Stockdale, G. Strong, S. Thais, K. Tomko, E. Upfal, E. Usai, A. Ustyuzhanin, M. Vala, S. Vallecorsa, J. Vasel, M. Verzetti, X. Vilasís-Cardona, J.-. Vlimant, I. Vukotic. - In: JOURNAL OF PHYSICS. CONFERENCE SERIES. - ISSN 1742-6588. - 1085:2(2018), pp. 022008.1-022008.27. ((Intervento presentato al 18. convegno International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 tenutosi a Seattle nel 2017 [10.1088/1742-6596/1085/2/022008].

Machine Learning in theoretical physics

S. Carrazza;
2018

Abstract

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.
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/615433
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