Machine Learning in High Energy Physics Community White Paper
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.
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Cite
@article{arxiv.1807.02876,
title = {Machine Learning in High Energy Physics Community White Paper},
author = {Kim Albertsson and Piero Altoe and Dustin Anderson and John Anderson and Michael Andrews and Juan Pedro Araque Espinosa and Adam Aurisano and Laurent Basara and Adrian Bevan and Wahid Bhimji and Daniele Bonacorsi and Bjorn Burkle and Paolo Calafiura and Mario Campanelli and Louis Capps and Federico Carminati and Stefano Carrazza and Yi-fan Chen and Taylor Childers and Yann Coadou and Elias Coniavitis and Kyle Cranmer and Claire David and Douglas Davis and Andrea De Simone and Javier Duarte and Martin Erdmann and Jonas Eschle and Amir Farbin and Matthew Feickert and Nuno Filipe Castro 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 Ulrich Heintz and Ben Hooberman and Johannes Junggeburth and Michael Kagan and Meghan Kane and Konstantin Kanishchev and Przemysław Karpiński 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 Köhler and Igor Lakomov and Kevin Lannon and Mario Lassnig and Antonio Limosani and Gilles Louppe and Aashrita Mangu and Pere Mato and Narain Meenakshi and Helge Meinhard and Dario Menasce and Lorenzo Moneta and Seth Moortgat and Mark Neubauer and Harvey Newman and Sydney Otten 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 Wei Sun and Savannah Thais and Karen Tomko and Eli Upfal and Emanuele Usai and Andrey Ustyuzhanin and Martin Vala and Justin Vasel and Sofia Vallecorsa and Mauro Verzetti and Xavier Vilasís-Cardona and Jean-Roch Vlimant and Ilija Vukotic and Sean-Jiun Wang and Gordon Watts and Michael Williams and Wenjing Wu and Stefan Wunsch and Kun Yang and Omar Zapata},
journal= {arXiv preprint arXiv:1807.02876},
year = {2019}
}
Comments
Editors: Sergei Gleyzer, Paul Seyfert and Steven Schramm