Search Menu
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information
Search Scholarly Publications

Title: Neural Networks for Modeling and Control of Particle Accelerators

Abstract

Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration betweenmore » Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.« less

Authors:
 [1];  [1];  [2];  [2];  [1];  [2]
+ Show Author Affiliations
  1. Colorado State Univ., Fort Collins, CO (United States)
  2. Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Publication Date:
Research Org.:
Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP)
OSTI Identifier:
1260272
Report Number(s):
FERMILAB-PUB-16-121-AD
Journal ID: ISSN 0018-9499; 1466580
Grant/Contract Number:  
AC02-07CH11359
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Transactions on Nuclear Science
Additional Journal Information:
Journal Volume: 63; Journal Issue: 2; Journal ID: ISSN 0018-9499
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Country of Publication:
United States
Language:
English
Subject:
43 PARTICLE ACCELERATORS; article intelligence; machine learning; particle accelerators; control systems; predictive control; adaptive control

Citation Formats

Edelen, A. L., Biedron, S. G., Chase, B. E., Edstrom, D., Milton, S. V., and Stabile, P. Neural Networks for Modeling and Control of Particle Accelerators. United States: N. p., 2016. Web. doi:10.1109/TNS.2016.2543203.
Copy to clipboard
Edelen, A. L., Biedron, S. G., Chase, B. E., Edstrom, D., Milton, S. V., & Stabile, P. Neural Networks for Modeling and Control of Particle Accelerators. United States. https://doi.org/10.1109/TNS.2016.2543203
Copy to clipboard
Edelen, A. L., Biedron, S. G., Chase, B. E., Edstrom, D., Milton, S. V., and Stabile, P. Fri . "Neural Networks for Modeling and Control of Particle Accelerators". United States. https://doi.org/10.1109/TNS.2016.2543203. https://www.osti.gov/servlets/purl/1260272.
Copy to clipboard
@article{osti_1260272,
title = {Neural Networks for Modeling and Control of Particle Accelerators},
author = {Edelen, A. L. and Biedron, S. G. and Chase, B. E. and Edstrom, D. and Milton, S. V. and Stabile, P.},
abstractNote = {Myriad nonlinear and complex physical phenomena are host to particle accelerators. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Moreover, many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. For the purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We also describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.},
doi = {10.1109/TNS.2016.2543203},
journal = {IEEE Transactions on Nuclear Science},
number = 2,
volume = 63,
place = {United States},
year = {2016},
month = {4}
}
Copy to clipboard
Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1109/TNS.2016.2543203
Copyright Statement
Other availability
Search WorldCat Search WorldCat to find libraries that may hold this journal
Citation Metrics:
Cited by: 40 works
Citation information provided by
Web of Science
Save / Share:
Export Metadata
Save to My Library
You must Sign In or Create an Account in order to save documents to your library.

Works referencing / citing this record:

Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
journal, June 2017

  • Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.
  • Nature Communications, Vol. 8, Issue 1
  • DOI: 10.1038/ncomms15461

Machine learning at the energy and intensity frontiers of particle physics
journal, August 2018

Accelerating lattice quantum Monte Carlo simulations using artificial neural networks: Application to the Holstein model
journal, July 2019

Model-Independent Tuning for Maximizing Free Electron Laser Pulse Energy
text, January 2019

  • Scheinker, Alexander; Bohler, Dorian; Tomin, Sergey
  • Deutsches Elektronen-Synchrotron, DESY, Hamburg
  • DOI: 10.3204/pubdb-2019-05406

Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning
text, January 2017

  • Sanchez-Gonzalez, A.; Micaelli, P.; Olivier, C.
  • Deutsches Elektronen-Synchrotron, DESY, Hamburg
  • DOI: 10.3204/pubdb-2018-00593
    Sort by:
    [ × clear filter / sort ]

    Similar Records in DOE PAGES and OSTI.GOV collections: