Machine Learning in High Energy Physics Community White Paper
Name
Albertsson_2018_J._Phys. _Conf._Ser._1085_022008.pdf
Description
Published version
Size
682.42 KB
Format
Adobe PDF
Checksum (MD5)
7b020521d4d9eadd6d322f5c2d0147c2
Author(s)
Albertsson, Kim
Altoe, Piero
Anderson, Dustin
Andrews, Michael
Araque Espinosa, Juan Pedro
Aurisano, Adam
Basara, Laurent
Bevan, Adrian
Bhimji, Wahid
Bonacorsi, Daniele
Date Issued
September 2018
Journal
Journal of Physics: Conference Series
Publisher
IOP Publishing
Citation
Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Andrews, Michael, Araque Espinosa, Juan Pedro et al. 2018. "Machine Learning in High Energy Physics Community White Paper." Journal of Physics: Conference Series, 1085 (2).
Version
Final published version
Abstract
© Published under licence by IOP Publishing Ltd. 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.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Creative Commons Attribution 3.0 unported license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1088/1742-6596/1085/2/022008
