Machine Learning in High Energy Physics Community White Paper

Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas DavisJavier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Meenakshi Narain, Mark Neubauer, Harvey Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Justin Vasel, Mauro Verzetti, Xavier Vilasís-Cardona, Jean Roch Vlimant, Ilija Vukotic, Sean Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

Research output: Contribution to journalConference article

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.

Original languageEnglish (US)
Article number022008
JournalJournal of Physics: Conference Series
Volume1085
Issue number2
DOIs
StatePublished - Oct 18 2018
Event18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 - Seattle, United States
Duration: Aug 21 2017Aug 25 2017

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machine learning
physics
research and development
resources
energy
explosions
hardware
education
neutrinos
industries
luminosity
computer programs
requirements

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Cite this

Albertsson, K., Altoe, P., Anderson, D., Andrews, M., Araque Espinosa, J. P., Aurisano, A., ... Zapata, O. (2018). Machine Learning in High Energy Physics Community White Paper. Journal of Physics: Conference Series, 1085(2), [022008]. https://doi.org/10.1088/1742-6596/1085/2/022008

Machine Learning in High Energy Physics Community White Paper. / 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; Castro, Nuno Filipe; 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; Mangu, Aashrita; Mato, Pere; Meinhard, Helge; Menasce, Dario; Moneta, Lorenzo; Moortgat, Seth; Narain, Meenakshi; 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; Vasel, Justin; Verzetti, Mauro; Vilasís-Cardona, Xavier; Vlimant, Jean Roch; Vukotic, Ilija; Wang, Sean Jiun; Watts, Gordon; Williams, Michael; Wu, Wenjing; Wunsch, Stefan; Zapata, Omar.

In: Journal of Physics: Conference Series, Vol. 1085, No. 2, 022008, 18.10.2018.

Research output: Contribution to journalConference article

Albertsson, K, Altoe, P, Anderson, D, Andrews, M, Araque Espinosa, JP, Aurisano, A, Basara, L, Bevan, A, Bhimji, W, Bonacorsi, D, Calafiura, P, Campanelli, M, Capps, L, Carminati, F, Carrazza, S, Childers, T, Coniavitis, E, Cranmer, K, David, C, Davis, D, Duarte, J, Erdmann, M, Eschle, J, Farbin, A, Feickert, M, Castro, NF, Fitzpatrick, C, Floris, M, Forti, A, Garra-Tico, J, Gemmler, J, Girone, M, Glaysher, P, Gleyzer, S, Gligorov, V, Golling, T, Graw, J, Gray, L, Greenwood, D, Hacker, T, Harvey, J, Hegner, B, Heinrich, L, Hooberman, B, Junggeburth, J, Kagan, M, Kane, M, Kanishchev, K, Karpiński, P, Kassabov, Z, Kaul, G, Kcira, D, Keck, T, Klimentov, A, Kowalkowski, J, Kreczko, L, Kurepin, A, Kutschke, R, Kuznetsov, V, Köhler, N, Lakomov, I, Lannon, K, Lassnig, M, Limosani, A, Louppe, G, Mangu, A, Mato, P, Meinhard, H, Menasce, D, Moneta, L, Moortgat, S, Narain, M, Neubauer, M, Newman, H, Pabst, H, Paganini, M, Paulini, M, Perdue, G, Perez, U, Picazio, A, Pivarski, J, Prosper, H, Psihas, F, Radovic, A, Reece, R, Rinkevicius, A, Rodrigues, E, Rorie, J, Rousseau, D, Sauers, A, Schramm, S, Schwartzman, A, Severini, H, Seyfert, P, Siroky, F, Skazytkin, K, Sokoloff, M, Stewart, G, Stienen, B, Stockdale, I, Strong, G, Thais, S, Tomko, K, Upfal, E, Usai, E, Ustyuzhanin, A, Vala, M, Vallecorsa, S, Vasel, J, Verzetti, M, Vilasís-Cardona, X, Vlimant, JR, Vukotic, I, Wang, SJ, Watts, G, Williams, M, Wu, W, Wunsch, S & Zapata, O 2018, 'Machine Learning in High Energy Physics Community White Paper', Journal of Physics: Conference Series, vol. 1085, no. 2, 022008. https://doi.org/10.1088/1742-6596/1085/2/022008
Albertsson K, Altoe P, Anderson D, Andrews M, Araque Espinosa JP, Aurisano A et al. Machine Learning in High Energy Physics Community White Paper. Journal of Physics: Conference Series. 2018 Oct 18;1085(2). 022008. https://doi.org/10.1088/1742-6596/1085/2/022008
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 ; Castro, Nuno Filipe ; 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 ; Mangu, Aashrita ; Mato, Pere ; Meinhard, Helge ; Menasce, Dario ; Moneta, Lorenzo ; Moortgat, Seth ; Narain, Meenakshi ; 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 ; Vasel, Justin ; Verzetti, Mauro ; Vilasís-Cardona, Xavier ; Vlimant, Jean Roch ; Vukotic, Ilija ; Wang, Sean Jiun ; Watts, Gordon ; Williams, Michael ; Wu, Wenjing ; Wunsch, Stefan ; Zapata, Omar. / Machine Learning in High Energy Physics Community White Paper. In: Journal of Physics: Conference Series. 2018 ; Vol. 1085, No. 2.
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AU - Araque Espinosa, Juan Pedro

AU - Aurisano, Adam

AU - Basara, Laurent

AU - Bevan, Adrian

AU - Bhimji, Wahid

AU - Bonacorsi, Daniele

AU - Calafiura, Paolo

AU - Campanelli, Mario

AU - Capps, Louis

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AU - Carrazza, Stefano

AU - Childers, Taylor

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AU - Gligorov, Vladimir

AU - Golling, Tobias

AU - Graw, Jonas

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AU - Greenwood, Dick

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AU - Kurepin, Alexander

AU - Kutschke, Rob

AU - Kuznetsov, Valentin

AU - Köhler, Nicolas

AU - Lakomov, Igor

AU - Lannon, Kevin

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AU - Limosani, Antonio

AU - Louppe, Gilles

AU - Mangu, Aashrita

AU - Mato, Pere

AU - Meinhard, Helge

AU - Menasce, Dario

AU - Moneta, Lorenzo

AU - Moortgat, Seth

AU - Narain, Meenakshi

AU - Neubauer, Mark

AU - Newman, Harvey

AU - Pabst, Hans

AU - Paganini, Michela

AU - Paulini, Manfred

AU - Perdue, Gabriel

AU - Perez, Uzziel

AU - Picazio, Attilio

AU - Pivarski, Jim

AU - Prosper, Harrison

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AU - Radovic, Alexander

AU - Reece, Ryan

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AU - Rodrigues, Eduardo

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AU - Rousseau, David

AU - Sauers, Aaron

AU - Schramm, Steven

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AU - Severini, Horst

AU - Seyfert, Paul

AU - Siroky, Filip

AU - Skazytkin, Konstantin

AU - Sokoloff, Mike

AU - Stewart, Graeme

AU - Stienen, Bob

AU - Stockdale, Ian

AU - Strong, Giles

AU - Thais, Savannah

AU - Tomko, Karen

AU - Upfal, Eli

AU - Usai, Emanuele

AU - Ustyuzhanin, Andrey

AU - Vala, Martin

AU - Vallecorsa, Sofia

AU - Vasel, Justin

AU - Verzetti, Mauro

AU - Vilasís-Cardona, Xavier

AU - Vlimant, Jean Roch

AU - Vukotic, Ilija

AU - Wang, Sean Jiun

AU - Watts, Gordon

AU - Williams, Michael

AU - Wu, Wenjing

AU - Wunsch, Stefan

AU - Zapata, Omar

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AB - 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.

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