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Machine Learning in High Energy Physics Community White Paper

Albertsson, Kim and Anderson, Dustin and Kcira, Dorian and Newman, Harvey and Vlimant, Jean-Roch (2018) Machine Learning in High Energy Physics Community White Paper. Journal of Physics: Conference Series, 1085 . Art. No. 022008. ISSN 1742-6596. https://resolver.caltech.edu/CaltechAUTHORS:20190606-111021195

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1088/1742-6596/1085/2/022008DOIArticle
ORCID:
AuthorORCID
Kcira, Dorian0000-0002-8190-2414
Newman, Harvey0000-0003-0964-1480
Additional Information:© 2018 Published under licence by IOP Publishing Ltd. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Record Number:CaltechAUTHORS:20190606-111021195
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190606-111021195
Official Citation:Kim Albertsson et al 2018 J. Phys.: Conf. Ser. 1085 022008
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:96192
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:06 Jun 2019 22:00
Last Modified:03 Oct 2019 21:20

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