IPAC2021 - List of Authors (Gonzalez-Aguilera, J.P.)

Paper	Title	Page
MOPAB304	Beam Diagnostics for Multi-Objective Bayesian Optimization at the Argonne Wakefield Accelerator Facility	960
	J.P. Gonzalez-Aguilera, Y.K. Kim University of Chicago, Chicago, Illinois, USA W. Liu, P. Piot, J.G. Power, E.E. Wisniewski ANL, Lemont, Illinois, USA R.J. Roussel Enrico Fermi Institute, University of Chicago, Chicago, Illinois, USA
	Particle accelerators must achieve certain beam quality objectives for use in different experiments. Usually, optimizing certain beam objectives comes at the expense of others. Additionally, there are many input parameters and a limited number of diagnostics. Therefore, accelerator tuning becomes a multi-objective optimization problem with a limited number of observations. Multi-objective Bayesian optimization was recently proposed as an efficient method to find the Pareto front for an online accelerator tuning problem with reduced number of observations. In order to experimentally test the multi-objective Bayesian optimization method, a novel accelerator diagnostic is being designed to measure multiple beam quality metrics of an electron beam at the Argonne Wakefield Accelerator Facility. Here, we present a design consisting in a pepper-pot mask, a dipole magnet and a scintillation screen, which allows a simultaneous measurement of the electron beam energy spread and vertical emittance. Additionally, a surrogate model for the vertical emittance was constructed with only 60 observations and without prior knowledge of the objective function nor diagnostics constraints.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB304
About •	paper received ※ 18 May 2021 paper accepted ※ 08 June 2021 issue date ※ 26 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Beam Diagnostics for Multi-Objective Bayesian Optimization at the Argonne Wakefield Accelerator Facility

960

J.P. Gonzalez-Aguilera, Y.K. Kim
University of Chicago, Chicago, Illinois, USA
W. Liu, P. Piot, J.G. Power, E.E. Wisniewski
ANL, Lemont, Illinois, USA
R.J. Roussel
Enrico Fermi Institute, University of Chicago, Chicago, Illinois, USA

Particle accelerators must achieve certain beam quality objectives for use in different experiments. Usually, optimizing certain beam objectives comes at the expense of others. Additionally, there are many input parameters and a limited number of diagnostics. Therefore, accelerator tuning becomes a multi-objective optimization problem with a limited number of observations. Multi-objective Bayesian optimization was recently proposed as an efficient method to find the Pareto front for an online accelerator tuning problem with reduced number of observations. In order to experimentally test the multi-objective Bayesian optimization method, a novel accelerator diagnostic is being designed to measure multiple beam quality metrics of an electron beam at the Argonne Wakefield Accelerator Facility. Here, we present a design consisting in a pepper-pot mask, a dipole magnet and a scintillation screen, which allows a simultaneous measurement of the electron beam energy spread and vertical emittance. Additionally, a surrogate model for the vertical emittance was constructed with only 60 observations and without prior knowledge of the objective function nor diagnostics constraints.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB304

About •

paper received ※ 18 May 2021 paper accepted ※ 08 June 2021 issue date ※ 26 August 2021

Export •

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