NAPAC2022 - List of Authors (Tran, A.D.)

Paper	Title	Page
MOPA89	RHIC Electron Beam Cooling Analysis Using Principle Component and Autoencoder Analysis	260
	A.D. Tran, Y. Hao FRIB, East Lansing, Michigan, USA X. Gu BNL, Upton, New York, USA
	Funding: Work supported by the US Department of Energy under contract No. DE-AC02-98CH10886. Principal component analysis and autoencoder analysis were used to analyze the experimental data of RHIC operation with low energy RHIC electron cooling (LEReC). This is unsupervised learning which includes electron beam settings and observable during operation. Both analyses were used to gauge the dimensional reducibility of the data and to understand which features are important to beam cooling.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA89
About •	Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 12 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

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

RHIC Electron Beam Cooling Analysis Using Principle Component and Autoencoder Analysis

260

A.D. Tran, Y. Hao
FRIB, East Lansing, Michigan, USA
X. Gu
BNL, Upton, New York, USA

Funding: Work supported by the US Department of Energy under contract No. DE-AC02-98CH10886.
Principal component analysis and autoencoder analysis were used to analyze the experimental data of RHIC operation with low energy RHIC electron cooling (LEReC). This is unsupervised learning which includes electron beam settings and observable during operation. Both analyses were used to gauge the dimensional reducibility of the data and to understand which features are important to beam cooling.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA89

About •

Received ※ 02 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 06 August 2022 — Issue date ※ 12 August 2022

Cite •

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

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)