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 Davis; Javier 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

doi:10.1088/1742-6596/1085/2/022008

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

Physics

Research output: Contribution to journal › Conference article › peer-review

132 Scopus citations

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 language	English (US)
Article number	022008
Journal	Journal of Physics: Conference Series
Volume	1085
Issue number	2
DOIs	https://doi.org/10.1088/1742-6596/1085/2/022008
State	Published - Oct 18 2018
Event	18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 - Seattle, United States Duration: Aug 21 2017 → Aug 25 2017

All Science Journal Classification (ASJC) codes

General Physics and Astronomy

Access to Document

10.1088/1742-6596/1085/2/022008

Cite this

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

@article{87ebcd89eda04d2cb63a66e889e134a1,

title = "Machine Learning in High Energy Physics Community White Paper",

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.",

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

note = "Publisher Copyright: {\textcopyright} Published under licence by IOP Publishing Ltd.; 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017 ; Conference date: 21-08-2017 Through 25-08-2017",

year = "2018",

month = oct,

day = "18",

doi = "10.1088/1742-6596/1085/2/022008",

language = "English (US)",

volume = "1085",

journal = "Journal of Physics: Conference Series",

issn = "1742-6588",

publisher = "IOP Publishing Ltd.",

number = "2",

}

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

TY - JOUR

T1 - Machine Learning in High Energy Physics Community White Paper

AU - Albertsson, Kim

AU - Altoe, Piero

AU - Anderson, Dustin

AU - Andrews, Michael

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

AU - Carminati, Federico

AU - Carrazza, Stefano

AU - Childers, Taylor

AU - Coniavitis, Elias

AU - Cranmer, Kyle

AU - David, Claire

AU - Davis, Douglas

AU - Duarte, Javier

AU - Erdmann, Martin

AU - Eschle, Jonas

AU - Farbin, Amir

AU - Feickert, Matthew

AU - Castro, Nuno Filipe

AU - Fitzpatrick, Conor

AU - Floris, Michele

AU - Forti, Alessandra

AU - Garra-Tico, Jordi

AU - Gemmler, Jochen

AU - Girone, Maria

AU - Glaysher, Paul

AU - Gleyzer, Sergei

AU - Gligorov, Vladimir

AU - Golling, Tobias

AU - Graw, Jonas

AU - Gray, Lindsey

AU - Greenwood, Dick

AU - Hacker, Thomas

AU - Harvey, John

AU - Hegner, Benedikt

AU - Heinrich, Lukas

AU - Hooberman, Ben

AU - Junggeburth, Johannes

AU - Kagan, Michael

AU - Kane, Meghan

AU - Kanishchev, Konstantin

AU - Karpiński, Przemysław

AU - Kassabov, Zahari

AU - Kaul, Gaurav

AU - Kcira, Dorian

AU - Keck, Thomas

AU - Klimentov, Alexei

AU - Kowalkowski, Jim

AU - Kreczko, Luke

AU - Kurepin, Alexander

AU - Kutschke, Rob

AU - Kuznetsov, Valentin

AU - Köhler, Nicolas

AU - Lakomov, Igor

AU - Lannon, Kevin

AU - Lassnig, Mario

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

AU - Psihas, Fernanda

AU - Radovic, Alexander

AU - Reece, Ryan

AU - Rinkevicius, Aurelius

AU - Rodrigues, Eduardo

AU - Rorie, Jamal

AU - Rousseau, David

AU - Sauers, Aaron

AU - Schramm, Steven

AU - Schwartzman, Ariel

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

PY - 2018/10/18

Y1 - 2018/10/18

N2 - 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.

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.

UR - http://www.scopus.com/inward/record.url?scp=85055779283&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85055779283&partnerID=8YFLogxK

U2 - 10.1088/1742-6596/1085/2/022008

DO - 10.1088/1742-6596/1085/2/022008

M3 - Conference article

AN - SCOPUS:85055779283

SN - 1742-6588

VL - 1085

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

IS - 2

M1 - 022008

T2 - 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, ACAT 2017

Y2 - 21 August 2017 through 25 August 2017

ER -

Machine Learning in High Energy Physics Community White Paper

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