IPAC2022 - List of Authors (Lin, W.)

Paper	Title	Page
TUPOST054	Experiment of Bayesian Optimization for Trajectory Alignment at Low Energy RHIC Electron Cooler	987
	Y. Gao, K.A. Brown, X. Gu, J. Morris, S. Seletskiy BNL, Upton, New York, USA J.A. Crittenden, G.H. Hoffstaetter, W. Lin Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
	Funding: Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy; U.S. National Science Foundation under Award PHY-1549132, the Center for Bright Beams. As the world’s first electron cooler that uses radio frequency (rf) accelerated electron bunches, the low energy RHIC electron cooling (LEReC) system is a nonmagnetized cooler of ion beams in RHIC at Brookhaven National Laboratory. Beam dynamics in LEReC are different from the more conventional electron coolers due to the bunching of the electron beam. To ensure an efficient cooling performance at LEReC, many parameters need to be monitored and fine-tuned. The alignment of the electron and ion trajectories in the LEReC cooling sections is one of the most critical parameters. This work explores using a machine learning (ML) method - Bayesian Optimization (BO) to optimize the trajectories’ alignment. Experimental results demonstrate that ML methods such as BO can perform control tasks efficiently in the RHIC controls system.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST054
About •	Received ※ 04 June 2022 — Revised ※ 11 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 27 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPOMS057	Simulation Studies and Machine Learning Applications at the Coherent electron Cooling experiment at RHIC	2387
	W. Lin, J.A. Crittenden, G.H. Hoffstaetter, M.A. Sampson Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA Y.C. Jing BNL, Upton, New York, USA K. Shih SBU, Stony Brook, New York, USA
	Funding: Work supported by the U.S. National Science Foundation under Award PHY-1549132, and by Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. Coherent electron cooling is a novel cooling technique which cools high-energy hadron beams rapidly by amplifying the modulation induced by hadrons in electron bunches. The Coherent electron cooling (CeC) experiment at Brookhaven National Laboratory (BNL) is a proof-of-principle test facility to demonstrate this technique. To achieve efficient cooling performance, electron beams generated in the CeC need to meet strict quality standards. In this work, we first present sensitivity studies of the low energy beam transport (LEBT) section, in preparation for building a surrogate model of the LEBT line in the future. We also present preliminary test results of a machine learning (ML) algorithm developed to improve the efficiency of slice-emittance measurements in the CeC diagnostic line.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS057
About •	Received ※ 06 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 15 June 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Experiment of Bayesian Optimization for Trajectory Alignment at Low Energy RHIC Electron Cooler

987

Y. Gao, K.A. Brown, X. Gu, J. Morris, S. Seletskiy
BNL, Upton, New York, USA
J.A. Crittenden, G.H. Hoffstaetter, W. Lin
Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA

Funding: Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy; U.S. National Science Foundation under Award PHY-1549132, the Center for Bright Beams.
As the world’s first electron cooler that uses radio frequency (rf) accelerated electron bunches, the low energy RHIC electron cooling (LEReC) system is a nonmagnetized cooler of ion beams in RHIC at Brookhaven National Laboratory. Beam dynamics in LEReC are different from the more conventional electron coolers due to the bunching of the electron beam. To ensure an efficient cooling performance at LEReC, many parameters need to be monitored and fine-tuned. The alignment of the electron and ion trajectories in the LEReC cooling sections is one of the most critical parameters. This work explores using a machine learning (ML) method - Bayesian Optimization (BO) to optimize the trajectories’ alignment. Experimental results demonstrate that ML methods such as BO can perform control tasks efficiently in the RHIC controls system.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-TUPOST054

About •

Received ※ 04 June 2022 — Revised ※ 11 June 2022 — Accepted ※ 13 June 2022 — Issue date ※ 27 June 2022

Cite •

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

WEPOMS057

Simulation Studies and Machine Learning Applications at the Coherent electron Cooling experiment at RHIC

2387

W. Lin, J.A. Crittenden, G.H. Hoffstaetter, M.A. Sampson
Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
Y.C. Jing
BNL, Upton, New York, USA
K. Shih
SBU, Stony Brook, New York, USA

Funding: Work supported by the U.S. National Science Foundation under Award PHY-1549132, and by Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy.
Coherent electron cooling is a novel cooling technique which cools high-energy hadron beams rapidly by amplifying the modulation induced by hadrons in electron bunches. The Coherent electron cooling (CeC) experiment at Brookhaven National Laboratory (BNL) is a proof-of-principle test facility to demonstrate this technique. To achieve efficient cooling performance, electron beams generated in the CeC need to meet strict quality standards. In this work, we first present sensitivity studies of the low energy beam transport (LEBT) section, in preparation for building a surrogate model of the LEBT line in the future. We also present preliminary test results of a machine learning (ML) algorithm developed to improve the efficiency of slice-emittance measurements in the CeC diagnostic line.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2022-WEPOMS057

About •

Received ※ 06 June 2022 — Accepted ※ 15 June 2022 — Issue date ※ 15 June 2022

Cite •

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