Machine Learning in High Energy Physics Community White Paper

Kim Albertsson , Piero Altoe , Dustin Anderson , John Anderson , Michael Andrews , Juan Pedro Araque Espinosa , Adam Aurisano , Laurent Basara , Adrian Bevan , Wahid Bhimji , Daniele Bonacorsi , Bjorn Burkle , Paolo Calafiura , Mario Campanelli , Louis Capps , Federico Carminati , Stefano Carrazza , Yi-Fan Chen , Taylor Childers , Yann Coadou , Elias Coniavitis , Kyle Cranmer , Claire David , Douglas Davis , Andrea de Simone , 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 (1) , Tobias Golling , Jonas Graw , Lindsey Gray , Dick Greenwood , Thomas Hacker , John Harvey , Benedikt Hegner , Lukas Heinrich , Ulrich Heintz , 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 , Meenakshi Narain , Helge Meinhard , Dario Menasce , Lorenzo Moneta , Seth Moortgat , Mark Neubauer , Harvey Newman , Sydney Otten , 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 (2) , Aaron Sauers , Steven Schramm , Ariel Schwartzman , Horst Severini , Paul Seyfert , Filip Siroky , Konstantin Skazytkin , Mike Sokoloff , Graeme Stewart , Bob Stienen , Ian Stockdale , Giles Strong , Wei Sun , Savannah Thais , Karen Tomko , Eli Upfal , Emanuele Usai , Andrey Ustyuzhanin , Martin Vala , Justin Vasel , Sofia Vallecorsa , Mauro Verzetti , Xavier Vilasís-Cardona , Jean-Roch Vlimant , Ilija Vukotic , Sean-Jiun Wang , Gordon Watts , Mike Williams , Wenjing Wu , Stefan Wunsch , Kun Yang , Omar Zapata

1 CNRS - Centre National de la Recherche Scientifique
2 UP11 - Université Paris-Sud - Paris 11

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.

Mots clés: programming; data analysis method; activity report; artificial intelligence

Domaines: Informatique [cs]; Physique des Hautes Energies - Expérience [hep-ex]; Physique [physics]

Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, et al.. Machine Learning in High Energy Physics Community White Paper. 18th International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Aug 2017, Seattle, United States. pp.022008, ⟨10.1088/1742-6596/1085/2/022008⟩. ⟨hal-01846718⟩