NAPAC2022 - List of Authors (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
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

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

Cite •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)