Machine Learning in High Energy Physics Community White Paper

Article
Report number	arXiv:1807.02876 ; FERMILAB-PUB-18-318-CD-DI-PPD
Title	Machine Learning in High Energy Physics Community White Paper
Author(s)	Albertsson, Kim (Lulea U.) ; Altoe, Piero (NVIDIA, Santa Clara) ; Anderson, Dustin (Caltech) ; Anderson, John ; Andrews, Michael (Carnegie Mellon U.) ; Araque Espinosa, Juan Pedro (LIP, Lisbon) ; Aurisano, Adam (Cincinnati U.) ; Basara, Laurent (INFN, Padua ; Padua U.) ; Bevan, Adrian (University Coll. London) ; Bhimji, Wahid (LBL, Berkeley) ; Bonacorsi, Daniele (INFN, Bologna ; U. Bologna, DIFA) ; Burkle, Bjorn ; Calafiura, Paolo (LBL, Berkeley) ; Campanelli, Mario (University Coll. London) ; Capps, Louis (NVIDIA, Santa Clara) ; Carminati, Federico (CERN) ; Carrazza, Stefano (CERN) ; Chen, Yi-fan ; Childers, Taylor (Argonne) ; Coadou, Yann ; Coniavitis, Elias (Freiburg U.) ; Cranmer, Kyle (New York U.) ; David, Claire (DESY) ; Davis, Douglas (Duke U.) ; De Simone, Andrea ; Duarte, Javier (Fermilab) ; Erdmann, Martin (RWTH Aachen U.) ; Eschle, Jonas (Zurich U.) ; Farbin, Amir (Texas U., Arlington) ; Feickert, Matthew (Southern Methodist U.) ; Castro, Nuno Filipe (LIP, Lisbon) ; Fitzpatrick, Conor (Ecole Polytechnique, Lausanne) ; Floris, Michele (CERN) ; Forti, Alessandra (Manchester U.) ; Garra-Tico, Jordi (Cambridge U.) ; Gemmler, Jochen (KIT, Karlsruhe) ; Girone, Maria (CERN) ; Glaysher, Paul (DESY) ; Gleyzer, Sergei (Florida U.) ; Gligorov, Vladimir (CNRS, France) ; Golling, Tobias (Geneva U.) ; Graw, Jonas (NVIDIA, Santa Clara) ; Gray, Lindsey (Fermilab) ; Greenwood, Dick (Louisiana Tech. U.) ; Hacker, Thomas (Purdue U.) ; Harvey, John (CERN) ; Hegner, Benedikt (CERN) ; Heinrich, Lukas (New York U.) ; Heintz, Ulrich ; Hooberman, Ben (Illinois U., Urbana) ; Junggeburth, Johannes (Munich, Max Planck Inst.) ; Kagan, Michael (SLAC) ; Kane, Meghan (Unlisted, DE) ; Kanishchev, Konstantin (INFN, Padua ; Padua U.) ; Karpiński, Przemysław (CERN) ; Kassabov, Zahari (Milan U.) ; Kaul, Gaurav (Caltech) ; Kcira, Dorian (Caltech) ; Keck, Thomas (KIT, Karlsruhe) ; Klimentov, Alexei (Brookhaven) ; Kowalkowski, Jim (Fermilab) ; Kreczko, Luke (Bristol U.) ; Kurepin, Alexander (ITAE, Moscow) ; Kutschke, Rob (Fermilab) ; Kuznetsov, Valentin (Cornell U.) ; Köhler, Nicolas (Munich, Max Planck Inst.) ; Lakomov, Igor (CERN) ; Lannon, Kevin (Notre Dame U.) ; Lassnig, Mario (CERN) ; Limosani, Antonio (Melbourne U.) ; Louppe, Gilles (New York U.) ; Mangu, Aashrita (UC, Berkeley) ; Mato, Pere (CERN) ; Narain, Meenakshi (Brown U.) ; Meinhard, Helge (CERN) ; Menasce, Dario (INFN, Milan Bicocca ; Milan Bicocca U.) ; Moneta, Lorenzo (CERN) ; Moortgat, Seth (Vrije U., Brussels) ; Neubauer, Mark (Illinois U., Urbana) ; Newman, Harvey (Caltech) ; Otten, Sydney ; Pabst, Hans (Caltech) ; Paganini, Michela (Yale U.) ; Paulini, Manfred (Carnegie Mellon U.) ; Perdue, Gabriel (Fermilab) ; Perez, Uzziel (Alabama U.) ; Picazio, Attilio (Massachusetts U., Amherst) ; Pivarski, Jim (Princeton U.) ; Prosper, Harrison (Florida State U.) ; Psihas, Fernanda (Indiana U.) ; Radovic, Alexander (William-Mary Coll.) ; Reece, Ryan (UC, Santa Cruz) ; Rinkevicius, Aurelius (Cornell U.) ; Rodrigues, Eduardo (Cincinnati U.) ; Rorie, Jamal (Rice U.) ; Rousseau, David (Orsay) ; Sauers, Aaron (Fermilab) ; Schramm, Steven (Geneva U.) ; Schwartzman, Ariel (SLAC) ; Severini, Horst (Oklahoma U.) ; Seyfert, Paul (CERN) ; Siroky, Filip (Masaryk U., Brno) ; Skazytkin, Konstantin (ITAE, Moscow) ; Sokoloff, Mike (Cincinnati U.) ; Stewart, Graeme (Glasgow U.) ; Stienen, Bob (Nijmegen U.) ; Stockdale, Ian (Bucharest, IFIN-HH) ; Strong, Giles (LIP, Lisbon) ; Sun, Wei ; Thais, Savannah (Yale U.) ; Tomko, Karen (OSC, Columbus) ; Upfal, Eli (Brown U.) ; Usai, Emanuele (Brown U.) ; Ustyuzhanin, Andrey (Yandex Sch. Data Anal., Moscow) ; Vala, Martin (Kosice Tech. U.) ; Vasel, Justin ; Vallecorsa, Sofia (Gangneung-Wonju Natl. U.) ; Verzetti, Mauro (Rochester U.) ; Vilasís-Cardona, Xavier (Barcelona U.) ; Vlimant, Jean-Roch (Caltech) ; Vukotic, Ilija (Chicago U.) ; Wang, Sean-Jiun (Florida U.) ; Watts, Gordon (Washington U., Seattle) ; Williams, Mike (MIT) ; Wu, Wenjing (ICTS, Beijing) ; Wunsch, Stefan (KIT, Karlsruhe) ; Yang, Kun ; Zapata, Omar (Antioquia U.)
Publication	2018-10-18
Imprint	2018-07-08
Number of pages	27
Note	Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm
In:	J. Phys.: Conf. Ser. 1085 (2018) 022008
In:	18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA, USA, 21 - 25 Aug 2017, pp.022008
DOI	10.1088/1742-6596/1085/2/022008
Subject category	stat.ML ; Mathematical Physics and Mathematics ; hep-ex ; Particle Physics - Experiment ; cs.LG ; Computing and Computers ; physics.comp-ph ; Other Fields of Physics
Abstract	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.
Copyright/License	preprint: (License: arXiv nonexclusive-distrib 1.0) publication: (License: CC-BY-3.0)