IPAC2021 - List of Authors (Kim, Y.K.)

Paper	Title	Page
MOXB02	First Results of the IOTA Ring Research at Fermilab	19
	A. Valishev, D.R. Broemmelsiek, A.V. Burov, K. Carlson, B.L. Cathey, S. Chattopadhyay, N. Eddy, D.R. Edstrom, J.D. Jarvis, V.A. Lebedev, S. Nagaitsev, H. Piekarz, A.L. Romanov, J. Ruan, J.K. Santucci, V.D. Shiltsev, G. Stancari Fermilab, Batavia, Illinois, USA A. Arodzero, A.Y. Murokh, M. Ruelas RadiaBeam, Santa Monica, California, USA D.L. Bruhwiler, J.P. Edelen, C.C. Hall RadiaSoft LLC, Boulder, Colorado, USA S. Chattopadhyay, S. Szustkowski Northern Illinois University, DeKalb, Illinois, USA A. Halavanau, Z. Huang, V. Yakimenko SLAC, Menlo Park, California, USA M. Hofer TU Vienna, Wien, Austria M. Hofer, R. Tomás García CERN, Geneva, Switzerland K. Hwang, C.E. Mitchell, R.D. Ryne LBNL, Berkeley, California, USA K.-J. Kim ANL, Lemont, Illinois, USA K.-J. Kim, Y.K. Kim, N. Kuklev, I. Lobach University of Chicago, Chicago, Illinois, USA T.V. Shaftan BNL, Upton, New York, USA
	Funding: Fermilab is operated by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the United States Department of Energy. The IOTA ring at Fermilab is a unique machine exclusively dedicated to accelerator beam physics R&D. The research conducted at IOTA includes topics such as nonlinear integrable optics, suppression of coherent beam instabilities, optical stochastic cooling and quantum science experiments. In this talk we report on the first results of experiments with implementations of nonlinear integrable beam optics. The first of its kind practical realization of a two-dimensional integrable system in a strongly-focusing storage ring was demonstrated allowing among other things for stable beam circulation near or at the integer resonance. Also presented will be the highlights of the world’s first demonstration of optical stochastic beam cooling and other selected results of IOTA’s broad experimental program.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOXB02
About •	paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 23 August 2021
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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
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TUXX02	Towards the Globatron for Tomorrow
	Y.K. Kim University of Chicago, Chicago, Illinois, USA
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TUPAB228	IOTA Run 2 Beam Dynamics Studies in Nonlinear Integrable Systems	1964
	N. Kuklev, Y.K. Kim University of Chicago, Chicago, Illinois, USA S. Nagaitsev, A.L. Romanov, A. Valishev Fermilab, Batavia, Illinois, USA
	Funding: Work supported by the U.S. NSF under award PHY-1549132, the Center for Bright Beams. Fermi Research Alliance, LLC operates Fermilab under Contract DE-AC02-07CH11359 with the US Department of Energy. Nonlinear integrable optics is a promising design approach for suppressing fast collective instabilities. To study it experimentally, a new storage ring, the Integrable Optics Test Accelerator (IOTA), was built at Fermilab. IOTA has recently completed its second scientific run, incorporating many hardware and instrumentation improvements. This report presents the results of the two integrable optics experiments - the quasi-integrable Henon-Heiles octupole system and the fully integrable Danilov-Nagaitsev system. We demonstrate tune spread and dynamic aperture in agreement with tracking simulations, and a stable crossing of the integer resonance. Based on recovered single-particle phase space dynamics, we show improved invariant jitter consistent with intended effective Hamiltonian. We conclude by outlining future plans and efforts towards proton studies and larger designs.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB228
About •	paper received ※ 31 May 2021 paper accepted ※ 23 June 2021 issue date ※ 10 August 2021
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WEPAB308	Measurement-Based Surrogate Model of the SLAC LCLS-II Injector	3395
	L. Gupta, Y.K. Kim University of Chicago, Chicago, Illinois, USA A.L. Edelen, C.E. Mayes, A.A. Mishra, N.R. Neveu SLAC, Menlo Park, California, USA
	Funding: This project was funded by the DOE SCGSR Program. There is significant effort within particle accelerator physics to use machine learning methods to improve modeling of accelerator components. Such models can be made realistic and representative of machine components by training them with measured data. These models could be used as virtual diagnostics or for model-based control when fast feedback is needed for tuning to different user settings. To prototype such a model, we demonstrate how a machine learning based surrogate model of the SLAC LCLS-II photocathode injector was developed. To create machine-based data, laser measurements were taken at the LCLS using the virtual cathode camera. These measurements were used to sample particles, resulting in realistic electron bunches, which were then propagated through the injector via the Astra space charge simulation. By doing this, the model is not only able to predict many bulk electron beam parameters and distributions which are often hard to measure or not usually available to measure, but the predictions are more realistic relative to traditionally simulated training data. The methods for training such models, as well as model capabilities and future work are presented here.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308
About •	paper received ※ 26 May 2021 paper accepted ※ 27 July 2021 issue date ※ 24 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

First Results of the IOTA Ring Research at Fermilab

19

A. Valishev, D.R. Broemmelsiek, A.V. Burov, K. Carlson, B.L. Cathey, S. Chattopadhyay, N. Eddy, D.R. Edstrom, J.D. Jarvis, V.A. Lebedev, S. Nagaitsev, H. Piekarz, A.L. Romanov, J. Ruan, J.K. Santucci, V.D. Shiltsev, G. Stancari
Fermilab, Batavia, Illinois, USA
A. Arodzero, A.Y. Murokh, M. Ruelas
RadiaBeam, Santa Monica, California, USA
D.L. Bruhwiler, J.P. Edelen, C.C. Hall
RadiaSoft LLC, Boulder, Colorado, USA
S. Chattopadhyay, S. Szustkowski
Northern Illinois University, DeKalb, Illinois, USA
A. Halavanau, Z. Huang, V. Yakimenko
SLAC, Menlo Park, California, USA
M. Hofer
TU Vienna, Wien, Austria
M. Hofer, R. Tomás García
CERN, Geneva, Switzerland
K. Hwang, C.E. Mitchell, R.D. Ryne
LBNL, Berkeley, California, USA
K.-J. Kim
ANL, Lemont, Illinois, USA
K.-J. Kim, Y.K. Kim, N. Kuklev, I. Lobach
University of Chicago, Chicago, Illinois, USA
T.V. Shaftan
BNL, Upton, New York, USA

Funding: Fermilab is operated by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the United States Department of Energy.
The IOTA ring at Fermilab is a unique machine exclusively dedicated to accelerator beam physics R&D. The research conducted at IOTA includes topics such as nonlinear integrable optics, suppression of coherent beam instabilities, optical stochastic cooling and quantum science experiments. In this talk we report on the first results of experiments with implementations of nonlinear integrable beam optics. The first of its kind practical realization of a two-dimensional integrable system in a strongly-focusing storage ring was demonstrated allowing among other things for stable beam circulation near or at the integer resonance. Also presented will be the highlights of the world’s first demonstration of optical stochastic beam cooling and other selected results of IOTA’s broad experimental program.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOXB02

About •

paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 23 August 2021

Export •

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

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)

TUXX02

Towards the Globatron for Tomorrow

Y.K. Kim
University of Chicago, Chicago, Illinois, USA

PLEASE ADD ABSTRACT

Export •

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

TUPAB228

IOTA Run 2 Beam Dynamics Studies in Nonlinear Integrable Systems

1964

N. Kuklev, Y.K. Kim
University of Chicago, Chicago, Illinois, USA
S. Nagaitsev, A.L. Romanov, A. Valishev
Fermilab, Batavia, Illinois, USA

Funding: Work supported by the U.S. NSF under award PHY-1549132, the Center for Bright Beams. Fermi Research Alliance, LLC operates Fermilab under Contract DE-AC02-07CH11359 with the US Department of Energy.
Nonlinear integrable optics is a promising design approach for suppressing fast collective instabilities. To study it experimentally, a new storage ring, the Integrable Optics Test Accelerator (IOTA), was built at Fermilab. IOTA has recently completed its second scientific run, incorporating many hardware and instrumentation improvements. This report presents the results of the two integrable optics experiments - the quasi-integrable Henon-Heiles octupole system and the fully integrable Danilov-Nagaitsev system. We demonstrate tune spread and dynamic aperture in agreement with tracking simulations, and a stable crossing of the integer resonance. Based on recovered single-particle phase space dynamics, we show improved invariant jitter consistent with intended effective Hamiltonian. We conclude by outlining future plans and efforts towards proton studies and larger designs.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB228

About •

paper received ※ 31 May 2021 paper accepted ※ 23 June 2021 issue date ※ 10 August 2021

Export •

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

WEPAB308

Measurement-Based Surrogate Model of the SLAC LCLS-II Injector

3395

L. Gupta, Y.K. Kim
University of Chicago, Chicago, Illinois, USA
A.L. Edelen, C.E. Mayes, A.A. Mishra, N.R. Neveu
SLAC, Menlo Park, California, USA

Funding: This project was funded by the DOE SCGSR Program.
There is significant effort within particle accelerator physics to use machine learning methods to improve modeling of accelerator components. Such models can be made realistic and representative of machine components by training them with measured data. These models could be used as virtual diagnostics or for model-based control when fast feedback is needed for tuning to different user settings. To prototype such a model, we demonstrate how a machine learning based surrogate model of the SLAC LCLS-II photocathode injector was developed. To create machine-based data, laser measurements were taken at the LCLS using the virtual cathode camera. These measurements were used to sample particles, resulting in realistic electron bunches, which were then propagated through the injector via the Astra space charge simulation. By doing this, the model is not only able to predict many bulk electron beam parameters and distributions which are often hard to measure or not usually available to measure, but the predictions are more realistic relative to traditionally simulated training data. The methods for training such models, as well as model capabilities and future work are presented here.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB308

About •

paper received ※ 26 May 2021 paper accepted ※ 27 July 2021 issue date ※ 24 August 2021

Export •

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