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Title: Machine Learning in High Energy Physics Community White Paper

Abstract

Machine learning is an important 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.

Authors:
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States); Brookhaven National Lab. (BNL), Upton, NY (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP) (SC-25)
OSTI Identifier:
1463622
Report Number(s):
FERMILAB-PUB-18-318-CD-DI-PPD; arXiv:1807.02876
1681439
DOE Contract Number:  
AC02-07CH11359
Resource Type:
Journal Article
Journal Name:
J.Phys.Conf.Ser.
Additional Journal Information:
Journal Volume: 1085; Journal Issue: 2
Country of Publication:
United States
Language:
English
Subject:
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS

Citation Formats

Albertsson, Kim, and et al. Machine Learning in High Energy Physics Community White Paper. United States: N. p., 2018. Web. doi:10.1088/1742-6596/1085/2/022008.
Albertsson, Kim, & et al. Machine Learning in High Energy Physics Community White Paper. United States. doi:10.1088/1742-6596/1085/2/022008.
Albertsson, Kim, and et al. Thu . "Machine Learning in High Energy Physics Community White Paper". United States. doi:10.1088/1742-6596/1085/2/022008. https://www.osti.gov/servlets/purl/1463622.
@article{osti_1463622,
title = {Machine Learning in High Energy Physics Community White Paper},
author = {Albertsson, Kim and et al.},
abstractNote = {Machine learning is an important 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.},
doi = {10.1088/1742-6596/1085/2/022008},
journal = {J.Phys.Conf.Ser.},
number = 2,
volume = 1085,
place = {United States},
year = {2018},
month = {10}
}

Journal Article:

Figures / Tables:

Figure 1 Figure 1: Existing data-formats used by ML communities.

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Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.