skip to main content

DOE PAGESDOE PAGES

Title: Machine learning-based longitudinal phase space prediction of particle accelerators

Here, we report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators. Our approach consists of training a ML-based virtual diagnostic to predict the LPS using only nondestructive linac and e-beam measurements as inputs. We validate this approach with a simulation study for the FACET-II linac and with an experimental demonstration conducted at LCLS. At LCLS, the e-beam LPS images are obtained with a transverse deflecting cavity and used as training data for our ML model. In both the FACET-II and LCLS cases we find good agreement between the predicted and simulated/measured LPS profiles, an important step towards showing the feasibility of implementing such a virtual diagnostic on particle accelerators in the future.
Authors:
 [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [1]
  1. SLAC National Accelerator Lab., Menlo Park, CA (United States)
Publication Date:
Grant/Contract Number:
AC02-76SF00515
Type:
Published Article
Journal Name:
Physical Review Accelerators and Beams
Additional Journal Information:
Journal Volume: 21; Journal Issue: 11; Journal ID: ISSN 2469-9888
Publisher:
American Physical Society (APS)
Research Org:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
43 PARTICLE ACCELERATORS
OSTI Identifier:
1482420
Alternate Identifier(s):
OSTI ID: 1490386

Emma, C., Edelen, A., Hogan, M. J., O’Shea, B., White, G., and Yakimenko, V.. Machine learning-based longitudinal phase space prediction of particle accelerators. United States: N. p., Web. doi:10.1103/physrevaccelbeams.21.112802.
Emma, C., Edelen, A., Hogan, M. J., O’Shea, B., White, G., & Yakimenko, V.. Machine learning-based longitudinal phase space prediction of particle accelerators. United States. doi:10.1103/physrevaccelbeams.21.112802.
Emma, C., Edelen, A., Hogan, M. J., O’Shea, B., White, G., and Yakimenko, V.. 2018. "Machine learning-based longitudinal phase space prediction of particle accelerators". United States. doi:10.1103/physrevaccelbeams.21.112802.
@article{osti_1482420,
title = {Machine learning-based longitudinal phase space prediction of particle accelerators},
author = {Emma, C. and Edelen, A. and Hogan, M. J. and O’Shea, B. and White, G. and Yakimenko, V.},
abstractNote = {Here, we report on the application of machine learning (ML) methods for predicting the longitudinal phase space (LPS) distribution of particle accelerators. Our approach consists of training a ML-based virtual diagnostic to predict the LPS using only nondestructive linac and e-beam measurements as inputs. We validate this approach with a simulation study for the FACET-II linac and with an experimental demonstration conducted at LCLS. At LCLS, the e-beam LPS images are obtained with a transverse deflecting cavity and used as training data for our ML model. In both the FACET-II and LCLS cases we find good agreement between the predicted and simulated/measured LPS profiles, an important step towards showing the feasibility of implementing such a virtual diagnostic on particle accelerators in the future.},
doi = {10.1103/physrevaccelbeams.21.112802},
journal = {Physical Review Accelerators and Beams},
number = 11,
volume = 21,
place = {United States},
year = {2018},
month = {11}
}