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Title: Machine Learning in High Energy Physics Community White Paper

Journal Article · · Journal of Physics. Conference Series
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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.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); Brookhaven National Laboratory (BNL), Upton, NY (United States); SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); Fermi National Accelerator Laboratory (FNAL), Batavia, IL (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), High Energy Physics (HEP)
DOE Contract Number:
AC02-07CH11359
OSTI ID:
1463622
Report Number(s):
FERMILAB-PUB-18-318-CD-DI-PPD; arXiv:1807.02876; 1681439
Journal Information:
Journal of Physics. Conference Series, Vol. 1085, Issue 2; Conference: 18.International Workshop on Advanced Computing and Analysis Techniques in Physics Research, Seattle, WA (United States), 21-25 Aug 2017; ISSN 1742-6588
Publisher:
IOP Publishing
Country of Publication:
United States
Language:
English

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Automation of the matrix element reweighting method text January 2010

Cited By (12)

Energy flow networks: deep sets for particle jets journal January 2019
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network journal November 2019
FPGA-Accelerated Machine Learning Inference as a Service for Particle Physics Computing journal October 2019
Machine learning and the physical sciences journal December 2019
Characterizing Magnetic Reconnection Regions Using Gaussian Mixture Models on Particle Velocity Distributions journal January 2020
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network text January 2019
Energy Flow Networks: Deep Sets for Particle Jets text January 2018
Study of energy deposition patterns in hadron calorimeter for prompt and displaced jets using convolutional neural network text January 2019
Covariantizing phase space journal November 2020
On the coverage of neutralino dark matter in coannihilations at the upgraded LHC journal March 2020
DeepRICH: Learning Deeply Cherenkov Detectors text January 2019
MLaaS4HEP: Machine Learning as a Service for HEP preprint January 2020

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