Author: Martinez Marin, J.L.
Paper Title Page
MOPA90 Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine Learning 263
  • A.D. Tran, Y. Hao
    FRIB, East Lansing, Michigan, USA
  • J.L. Martinez Marin, B. Mustapha
    ANL, Lemont, Illinois, USA
  Funding: This work was supported by a sub-reward from Argonne National Laboratory and supported by the U.S. Department of Energy, under Contract No. DE-AC02-06CH11357.
This work demonstrates using a Neural Network and a Gaussian Process to model the ATLAS front-end. Various neural network architectures were created and trained on the machine settings and outputs to model the phase space projections. The model was then trained on a dataset, with non-linear distortion, to gauge the transferability of the model from simulation to machine.
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA90  
About • Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 11 September 2022
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