IPAC2021 - List of Authors (Brown, K.A.)

Paper	Title	Page
MOPAB009	Review of the Fixed Target Operation at RHIC in 2020	69
	C. Liu, P. Adams, E.N. Beebe, S. Binello, I. Blackler, M. Blaskiewicz, K.A. Brown, D. Bruno, B.D. Coe, K.A. Drees, A.V. Fedotov, W. Fischer, C.J. Gardner, C.E. Giorgio, X. Gu, T. Hayes, K. Hock, H. Huang, R.L. Hulsart, T. Kanesue, D. Kayran, N.A. Kling, B. Lepore, Y. Luo, D. Maffei, G.J. Marr, A. Marusic, K. Mernick, R.J. Michnoff, M.G. Minty, J. Morris, C. Naylor, S. Nemesure, M. Okamura, I. Pinayev, S. Polizzo, D. Raparia, G. Robert-Demolaize, T. Roser, J. Sandberg, V. Schoefer, S. Seletskiy, F. Severino, T.C. Shrey, P. Thieberger, M. Valette, A. Zaltsman, I. Zane, K. Zeno, W. Zhang BNL, Upton, New York, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. As part of the Beam Energy Scan (BES) physics program, RHIC operated in Fixed Target mode at various beam energies in 2020. The fixed target experiment, achieved by scraping the beam halo of the circulating beam on a gold ring inserted in the beam pipe upstream of the experimental detectors, extends the range of the center-of-mass energy for BES. The machine configuration, control of rates, and results of the fixed target experiment operation in 2020 will be presented in this report.
	Poster MOPAB009 [2.913 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB009
About •	paper received ※ 16 May 2021 paper accepted ※ 17 August 2021 issue date ※ 23 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPAB010	RHIC Beam Energy Scan Operation with Electron Cooling in 2020	72
	C. Liu, P. Adams, E.N. Beebe, S. Binello, I. Blackler, M. Blaskiewicz, K.A. Brown, D. Bruno, B.D. Coe, K.A. Drees, A.V. Fedotov, W. Fischer, C.J. Gardner, C.E. Giorgio, X. Gu, T. Hayes, K. Hock, H. Huang, R.L. Hulsart, T. Kanesue, D. Kayran, N.A. Kling, B. Lepore, Y. Luo, D. Maffei, G.J. Marr, A. Marusic, K. Mernick, R.J. Michnoff, M.G. Minty, J. Morris, C. Naylor, S. Nemesure, M. Okamura, I. Pinayev, S. Polizzo, D. Raparia, G. Robert-Demolaize, T. Roser, J. Sandberg, V. Schoefer, S. Seletskiy, F. Severino, T.C. Shrey, P. Thieberger, M. Valette, A. Zaltsman, I. Zane, K. Zeno, W. Zhang BNL, Upton, New York, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. RHIC provided Au-Au collisions at beam energies of 5.75 and 4.59 GeV/nucleon for the physics program in 2020 as a part of the Beam Energy Scan II experiment. The operational experience at these energies will be reported with emphasis on their unique features. These unique features include the addition of a third harmonic RF system to enable a large longitudinal acceptance at 5.75 GeV/nucleon, the application of additional lower frequency cavities for alleviating space charge effects, and the world-first operation of cooling with an RF-accelerated bunched electron beam.
	Poster MOPAB010 [3.523 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB010
About •	paper received ※ 17 May 2021 paper accepted ※ 29 July 2021 issue date ※ 10 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPAB286	Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System	906
	M.A. Fazio, S. Biedron, M. Martínez-Ramón, D.J. Monk, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA M. Babzien, K.A. Brown, M.G. Fedurin, J.J. Li, M.A. Palmer, J. Tao BNL, Upton, New York, USA S. Biedron, T. Talbott UNM-ME, Albuquerque, New Mexico, USA J. Chen, A.J. Hurd, N.A. Moody, R. Prasankumar, C. Sweeney LANL, Los Alamos, New Mexico, USA D. Martin, M.E. Papka ANL, Lemont, Illinois, USA
	Funding: US DOE, SC, BES, MSE, award DE-SC0021365 and DOE NNSA award 89233218CNA000001 through DOE’s EPSCoR program in Office of BES with resources of DOE SC User Facilities BNL’s ATF and ALCF. A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB286
About •	paper received ※ 20 May 2021 paper accepted ※ 09 June 2021 issue date ※ 25 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPAB314	Surrogate Modeling for MUED with Neural Networks	970
	D.J. Monk, S. Biedron, M.A. Fazio, M. Martínez-Ramón, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA M. Babzien, K.A. Brown, M.A. Palmer, J. Tao BNL, Upton, New York, USA D. Martin, M.E. Papka ANL, Lemont, Illinois, USA T. Talbott UNM-ME, Albuquerque, New Mexico, USA
	Electron diffraction is among the most complex and influential inventions of the last century and contributes to research in many areas of physics and engineering. Not only does it aid in problems like materials and plasma research, electron diffraction systems like the MeV ultra-fast electron diffraction(MUED) instrument at the Brookhaven National Lab(BNL) also present opportunities to explore/implement surrogate modeling methods using artificial intelligence/machine learning/deep learning algorithms. Running the MUED system requires extended periods of uninterrupted runtime, skilled operators, and many varying parameters that depend on the desired output. These problems lend themselves to techniques based on neural networks(NNs), which are suited to modeling, system controls, and analysis of time-varying/multi-parameter systems. NNs can be deployed in model-based control areas and can be used simulate control designs, planned experiments, and to simulate employment of new components. Surrogate models based on NNs provide fast and accurate results, ideal for real-time control systems during continuous operation and may be used to identify irregular beam behavior as they develop.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB314
About •	paper received ※ 20 May 2021 paper accepted ※ 07 June 2021 issue date ※ 15 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB005	Design Status Update of the Electron-Ion Collider	2585
	C. Montag, E.C. Aschenauer, G. Bassi, J. Beebe-Wang, J.S. Berg, M. Blaskiewicz, A. Blednykh, J.M. Brennan, S.J. Brooks, K.A. Brown, Z.A. Conway, K.A. Drees, A.V. Fedotov, W. Fischer, C. Folz, D.M. Gassner, X. Gu, R.C. Gupta, Y. Hao, A. Hershcovitch, C. Hetzel, D. Holmes, H. Huang, W.A. Jackson, J. Kewisch, Y. Li, C. Liu, H. Lovelace III, Y. Luo, M. Mapes, D. Marx, G.T. McIntyre, F. Méot, M.G. Minty, S.K. Nayak, R.B. Palmer, B. Parker, S. Peggs, B. Podobedov, V. Ptitsyn, V.H. Ranjbar, G. Robert-Demolaize, S. Seletskiy, V.V. Smaluk, K.S. Smith, S. Tepikian, R. Than, P. Thieberger, D. Trbojevic, N. Tsoupas, J.E. Tuozzolo, S. Verdú-Andrés, E. Wang, D. Weiss, F.J. Willeke, H. Witte, Q. Wu, W. Xu, A. Zaltsman, W. Zhang BNL, Upton, New York, USA S.V. Benson, J.M. Grames, F. Lin, T.J. Michalski, V.S. Morozov, E.A. Nissen, J.P. Preble, R.A. Rimmer, T. Satogata, A. Seryi, M. Wiseman, W. Wittmer, Y. Zhang JLab, Newport News, Virginia, USA Y. Cai, Y.M. Nosochkov, G. Stupakov, M.K. Sullivan SLAC, Menlo Park, California, USA K.E. Deitrick, C.M. Gulliford, G.H. Hoffstaetter, J.E. Unger Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA E. Gianfelice-Wendt Fermilab, Batavia, Illinois, USA T. Satogata ODU, Norfolk, Virginia, USA D. Xu FRIB, East Lansing, Michigan, USA
	Funding: Work supported by BSA, LLC under Contract No. DE-SC0012704, by JSA, LLC under Contract No. DE-AC05-06OR23177, and by SLAC under Contract No. DE-AC02-76SF00515 with the U.S. Department of Energy. The design of the electron-ion collider EIC to be constructed at Brookhaven National Laboratory has been continuously evolving towards a realistic and robust design that meets all the requirements set forth by the nuclear physics community in the White Paper. Over the past year activities have been focused on maturing the design, and on developing alternatives to mitigate risk. These include improvements of the interaction region design as well as modifications of the hadron ring vacuum system to accommodate the high average and peak beam currents. Beam dynamics studies have been performed to determine and optimize the dynamic aperture in the two collider rings and the beam-beam performance. We will present the EIC design with a focus on recent developments.
	Poster WEPAB005 [2.095 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB005
About •	paper received ※ 14 May 2021 paper accepted ※ 22 June 2021 issue date ※ 16 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB306	Applying Machine Learning to Optimization of Cooling Rate at Low Energy RHIC Electron Cooler	3391
	Y. Gao, K.A. Brown, P.S. Dyer, S. Seletskiy, H. Zhao BNL, Upton, New York, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy. The Low Energy RHIC electron Cooler (LEReC) is a novel, state-of-the-art, electron accelerator for cooling RHIC ion beams, which was recently built and commissioned. Optimization of cooling with LEReC requires fine-tuning of numerous LEReC parameters. In this work, initial optimization results of using Machine Learning (ML) methods - Bayesian Optimization (BO) and Q-learning are presented. Specially, we focus on exploring the influence of the electron trajectory on the cooling rate. In the first part, simulations are conducted by utilizing a LEReC simulator. The results show that both methods have the capability of deriving electron positions that can optimize the cooling rate. Moreover, BO takes fewer samples to converge than the Q-learning method. In the second part, Bayesian optimization is further trained on the historical cooling data. In the new samples generated by the BO, the percentage of larger cooling rates data is greatly enhanced compared with the original historical data.
	Poster WEPAB306 [1.083 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB306
About •	paper received ※ 12 May 2021 paper accepted ※ 01 July 2021 issue date ※ 24 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB411	Ion Coulomb Crystals in Storage Rings for Quantum Information Science	3667
	K.A. Brown, G.J. Mahler, T. Roser, T.V. Shaftan, Z. Zhao BNL, Upton, New York, USA A. Aslam, S. Biedron, T.B. Bolin, C. Gonzalez-Zacarias, S.I. Sosa Guitron UNM-ECE, Albuquerque, USA R. Chen, T.G. Robertazzi Stony Brook University, Stony Brook, New York, USA B. Huang SBU, Stony Brook, USA
	Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy. We discuss the possible use of crystalline beams in storage rings for applications in quantum information science (QIS). Crystalline beams have been created in ion trap systems and proven to be useful as a computational basis for QIS applications. The same structures can be created in a storage ring, but the ions necessarily have a constant velocity and are rotating in a circular trap. The basic structures that are needed are ultracold crystalline beams, called ion Coulomb crystals (ICC’s). We will describe different applications of ICC’s for QIS, how QIS information is obtained and can be used for quantum computing, and some of the challenges that need to be resolved to realize practical QIS applications in storage rings.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB411
About •	paper received ※ 19 May 2021 paper accepted ※ 20 July 2021 issue date ※ 20 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Review of the Fixed Target Operation at RHIC in 2020

69

C. Liu, P. Adams, E.N. Beebe, S. Binello, I. Blackler, M. Blaskiewicz, K.A. Brown, D. Bruno, B.D. Coe, K.A. Drees, A.V. Fedotov, W. Fischer, C.J. Gardner, C.E. Giorgio, X. Gu, T. Hayes, K. Hock, H. Huang, R.L. Hulsart, T. Kanesue, D. Kayran, N.A. Kling, B. Lepore, Y. Luo, D. Maffei, G.J. Marr, A. Marusic, K. Mernick, R.J. Michnoff, M.G. Minty, J. Morris, C. Naylor, S. Nemesure, M. Okamura, I. Pinayev, S. Polizzo, D. Raparia, G. Robert-Demolaize, T. Roser, J. Sandberg, V. Schoefer, S. Seletskiy, F. Severino, T.C. Shrey, P. Thieberger, M. Valette, A. Zaltsman, I. Zane, K. Zeno, W. Zhang
BNL, Upton, New York, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
As part of the Beam Energy Scan (BES) physics program, RHIC operated in Fixed Target mode at various beam energies in 2020. The fixed target experiment, achieved by scraping the beam halo of the circulating beam on a gold ring inserted in the beam pipe upstream of the experimental detectors, extends the range of the center-of-mass energy for BES. The machine configuration, control of rates, and results of the fixed target experiment operation in 2020 will be presented in this report.

Poster MOPAB009 [2.913 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB009

About •

paper received ※ 16 May 2021 paper accepted ※ 17 August 2021 issue date ※ 23 August 2021

Export •

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

MOPAB010

RHIC Beam Energy Scan Operation with Electron Cooling in 2020

72

C. Liu, P. Adams, E.N. Beebe, S. Binello, I. Blackler, M. Blaskiewicz, K.A. Brown, D. Bruno, B.D. Coe, K.A. Drees, A.V. Fedotov, W. Fischer, C.J. Gardner, C.E. Giorgio, X. Gu, T. Hayes, K. Hock, H. Huang, R.L. Hulsart, T. Kanesue, D. Kayran, N.A. Kling, B. Lepore, Y. Luo, D. Maffei, G.J. Marr, A. Marusic, K. Mernick, R.J. Michnoff, M.G. Minty, J. Morris, C. Naylor, S. Nemesure, M. Okamura, I. Pinayev, S. Polizzo, D. Raparia, G. Robert-Demolaize, T. Roser, J. Sandberg, V. Schoefer, S. Seletskiy, F. Severino, T.C. Shrey, P. Thieberger, M. Valette, A. Zaltsman, I. Zane, K. Zeno, W. Zhang
BNL, Upton, New York, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
RHIC provided Au-Au collisions at beam energies of 5.75 and 4.59 GeV/nucleon for the physics program in 2020 as a part of the Beam Energy Scan II experiment. The operational experience at these energies will be reported with emphasis on their unique features. These unique features include the addition of a third harmonic RF system to enable a large longitudinal acceptance at 5.75 GeV/nucleon, the application of additional lower frequency cavities for alleviating space charge effects, and the world-first operation of cooling with an RF-accelerated bunched electron beam.

Poster MOPAB010 [3.523 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB010

About •

paper received ※ 17 May 2021 paper accepted ※ 29 July 2021 issue date ※ 10 August 2021

Export •

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

MOPAB286

Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System

906

M.A. Fazio, S. Biedron, M. Martínez-Ramón, D.J. Monk, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
M. Babzien, K.A. Brown, M.G. Fedurin, J.J. Li, M.A. Palmer, J. Tao
BNL, Upton, New York, USA
S. Biedron, T. Talbott
UNM-ME, Albuquerque, New Mexico, USA
J. Chen, A.J. Hurd, N.A. Moody, R. Prasankumar, C. Sweeney
LANL, Los Alamos, New Mexico, USA
D. Martin, M.E. Papka
ANL, Lemont, Illinois, USA

Funding: US DOE, SC, BES, MSE, award DE-SC0021365 and DOE NNSA award 89233218CNA000001 through DOE’s EPSCoR program in Office of BES with resources of DOE SC User Facilities BNL’s ATF and ALCF.
A MeV ultrafast electron diffraction (MUED) instrument is a unique characterization technique to study ultrafast processes in materials by a pump-probe technique. This relatively young technology can be advanced further into a turn-key instrument by using data science and artificial intelligence (AI) mechanisms in conjunctions with high-performance computing. This can facilitate automated operation, data acquisition and real time or near- real time processing. AI based system controls can provide real time feedback on the electron beam which is currently not possible due to the use of destructive diagnostics. Deep learning can be applied to the MUED diffraction patterns to recover valuable information on subtle lattice variations that can lead to a greater understanding of a wide range of material systems. A data science enabled MUED facility will also facilitate the application of this technique, expand its user base, and provide a fully automated state-of-the-art instrument. We will discuss the progress made on the MUED instrument in the Accelerator Test Facility of Brookhaven National Laboratory.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB286

About •

paper received ※ 20 May 2021 paper accepted ※ 09 June 2021 issue date ※ 25 August 2021

Export •

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

MOPAB314

Surrogate Modeling for MUED with Neural Networks

970

D.J. Monk, S. Biedron, M.A. Fazio, M. Martínez-Ramón, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
M. Babzien, K.A. Brown, M.A. Palmer, J. Tao
BNL, Upton, New York, USA
D. Martin, M.E. Papka
ANL, Lemont, Illinois, USA
T. Talbott
UNM-ME, Albuquerque, New Mexico, USA

Electron diffraction is among the most complex and influential inventions of the last century and contributes to research in many areas of physics and engineering. Not only does it aid in problems like materials and plasma research, electron diffraction systems like the MeV ultra-fast electron diffraction(MUED) instrument at the Brookhaven National Lab(BNL) also present opportunities to explore/implement surrogate modeling methods using artificial intelligence/machine learning/deep learning algorithms. Running the MUED system requires extended periods of uninterrupted runtime, skilled operators, and many varying parameters that depend on the desired output. These problems lend themselves to techniques based on neural networks(NNs), which are suited to modeling, system controls, and analysis of time-varying/multi-parameter systems. NNs can be deployed in model-based control areas and can be used simulate control designs, planned experiments, and to simulate employment of new components. Surrogate models based on NNs provide fast and accurate results, ideal for real-time control systems during continuous operation and may be used to identify irregular beam behavior as they develop.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB314

About •

paper received ※ 20 May 2021 paper accepted ※ 07 June 2021 issue date ※ 15 August 2021

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

WEPAB005

Design Status Update of the Electron-Ion Collider

2585

C. Montag, E.C. Aschenauer, G. Bassi, J. Beebe-Wang, J.S. Berg, M. Blaskiewicz, A. Blednykh, J.M. Brennan, S.J. Brooks, K.A. Brown, Z.A. Conway, K.A. Drees, A.V. Fedotov, W. Fischer, C. Folz, D.M. Gassner, X. Gu, R.C. Gupta, Y. Hao, A. Hershcovitch, C. Hetzel, D. Holmes, H. Huang, W.A. Jackson, J. Kewisch, Y. Li, C. Liu, H. Lovelace III, Y. Luo, M. Mapes, D. Marx, G.T. McIntyre, F. Méot, M.G. Minty, S.K. Nayak, R.B. Palmer, B. Parker, S. Peggs, B. Podobedov, V. Ptitsyn, V.H. Ranjbar, G. Robert-Demolaize, S. Seletskiy, V.V. Smaluk, K.S. Smith, S. Tepikian, R. Than, P. Thieberger, D. Trbojevic, N. Tsoupas, J.E. Tuozzolo, S. Verdú-Andrés, E. Wang, D. Weiss, F.J. Willeke, H. Witte, Q. Wu, W. Xu, A. Zaltsman, W. Zhang
BNL, Upton, New York, USA
S.V. Benson, J.M. Grames, F. Lin, T.J. Michalski, V.S. Morozov, E.A. Nissen, J.P. Preble, R.A. Rimmer, T. Satogata, A. Seryi, M. Wiseman, W. Wittmer, Y. Zhang
JLab, Newport News, Virginia, USA
Y. Cai, Y.M. Nosochkov, G. Stupakov, M.K. Sullivan
SLAC, Menlo Park, California, USA
K.E. Deitrick, C.M. Gulliford, G.H. Hoffstaetter, J.E. Unger
Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
E. Gianfelice-Wendt
Fermilab, Batavia, Illinois, USA
T. Satogata
ODU, Norfolk, Virginia, USA
D. Xu
FRIB, East Lansing, Michigan, USA

Funding: Work supported by BSA, LLC under Contract No. DE-SC0012704, by JSA, LLC under Contract No. DE-AC05-06OR23177, and by SLAC under Contract No. DE-AC02-76SF00515 with the U.S. Department of Energy.
The design of the electron-ion collider EIC to be constructed at Brookhaven National Laboratory has been continuously evolving towards a realistic and robust design that meets all the requirements set forth by the nuclear physics community in the White Paper. Over the past year activities have been focused on maturing the design, and on developing alternatives to mitigate risk. These include improvements of the interaction region design as well as modifications of the hadron ring vacuum system to accommodate the high average and peak beam currents. Beam dynamics studies have been performed to determine and optimize the dynamic aperture in the two collider rings and the beam-beam performance. We will present the EIC design with a focus on recent developments.

Poster WEPAB005 [2.095 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB005

About •

paper received ※ 14 May 2021 paper accepted ※ 22 June 2021 issue date ※ 16 August 2021

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WEPAB306

Applying Machine Learning to Optimization of Cooling Rate at Low Energy RHIC Electron Cooler

3391

Y. Gao, K.A. Brown, P.S. Dyer, S. Seletskiy, H. Zhao
BNL, Upton, New York, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-SC0012704 with the U.S. Department of Energy.
The Low Energy RHIC electron Cooler (LEReC) is a novel, state-of-the-art, electron accelerator for cooling RHIC ion beams, which was recently built and commissioned. Optimization of cooling with LEReC requires fine-tuning of numerous LEReC parameters. In this work, initial optimization results of using Machine Learning (ML) methods - Bayesian Optimization (BO) and Q-learning are presented. Specially, we focus on exploring the influence of the electron trajectory on the cooling rate. In the first part, simulations are conducted by utilizing a LEReC simulator. The results show that both methods have the capability of deriving electron positions that can optimize the cooling rate. Moreover, BO takes fewer samples to converge than the Q-learning method. In the second part, Bayesian optimization is further trained on the historical cooling data. In the new samples generated by the BO, the percentage of larger cooling rates data is greatly enhanced compared with the original historical data.

Poster WEPAB306 [1.083 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB306

About •

paper received ※ 12 May 2021 paper accepted ※ 01 July 2021 issue date ※ 24 August 2021

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WEPAB411

Ion Coulomb Crystals in Storage Rings for Quantum Information Science

3667

K.A. Brown, G.J. Mahler, T. Roser, T.V. Shaftan, Z. Zhao
BNL, Upton, New York, USA
A. Aslam, S. Biedron, T.B. Bolin, C. Gonzalez-Zacarias, S.I. Sosa Guitron
UNM-ECE, Albuquerque, USA
R. Chen, T.G. Robertazzi
Stony Brook University, Stony Brook, New York, USA
B. Huang
SBU, Stony Brook, USA

Funding: Work supported by Brookhaven Science Associates, LLC under Contract No. DE-AC02-98CH10886 with the U.S. Department of Energy.
We discuss the possible use of crystalline beams in storage rings for applications in quantum information science (QIS). Crystalline beams have been created in ion trap systems and proven to be useful as a computational basis for QIS applications. The same structures can be created in a storage ring, but the ions necessarily have a constant velocity and are rotating in a circular trap. The basic structures that are needed are ultracold crystalline beams, called ion Coulomb crystals (ICC’s). We will describe different applications of ICC’s for QIS, how QIS information is obtained and can be used for quantum computing, and some of the challenges that need to be resolved to realize practical QIS applications in storage rings.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB411

About •

paper received ※ 19 May 2021 paper accepted ※ 20 July 2021 issue date ※ 20 August 2021

Export •

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