NAPAC2022 - List of Keywords (ion-source)

Paper	Title	Other Keywords	Page
MOPA44	Utilizing Python to Prepare the VENUS Ion Source for Machine Learning	controls, PLC, interface, ECR	151
	A. Kireeff, L. Phair, M.J. Regis, M. Salathe, D.S. Todd LBNL, Berkeley, California, USA
	Funding: This work was supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC02-05CH11231. The fully superconducting electron cyclotron resonance (ECR) ion source VENUS is one of the world’s two highest-performing ECR ion sources, and a copy of this source will soon be used to produce ion beams at FRIB. The tuning and optimization of ECR ion sources is time consuming, and there are few detailed theoretical models to guide this work. To aid in this process, we are working toward utilizing machine learning to both efficiently optimize VENUS and reliably maintain its stability for long campaigns. We have created a Python library to interface with the programmable logic controller (PLC) in order to operate VENUS and collect and store source and beam data. We will discuss the design and safety considerations that went into creating this library, the implementation of the library, and some of the capabilities it enables.
	Poster MOPA44 [0.862 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA44
About •	Received ※ 17 July 2022 — Revised ※ 27 July 2022 — Accepted ※ 05 August 2022 — Issue date ※ 16 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA05	An H⁻ Injector for the ESS Storage Ring	cathode, rfq, plasma, operation	357
	V.G. Dudnikov, M.A. Cummings, M. Popovic Muons, Inc, Illinois, USA
	H⁻ charge exchange (stripping) injection into the European Spallation neutron Source (ESS) Storage Ring requires a 90 mA H⁻ ion source that delivers 2.9 ms pulses at 14 Hz repetition rate (duty factor ~4%) that can be extended to 28 Hz (df 8%). This can be achieved with a magnetron surface plasma H⁻ source (SPS) with active cathode and anode cooling. The Brookhaven National Laboratory (BNL) magnetron SPS can produce an H⁻ beam current of 100 mA with about 2 kW discharge power and can operate up to 0.7 % duty factor (average power 14 W) without active cooling. We describe how active cathode and anode cooling can be applied to the BNL source to increase the average discharge power up to 140 W (df 8%) to satisfy the needs of the ESS. We also describe the use of a short electrostatic LEBT as is used at the Oak Ridge National Laboratory Spallation Neutron Source to improve the beam delivery to the RFQ.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA05
About •	Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 04 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPA65	Machine Learning for the LANL Electromagnetic Isotope Separator	controls, dipole, feedback, electron	490
	A. Scheinker, K.W. Dudeck, C.P. Leibman LANL, Los Alamos, New Mexico, USA
	Funding: Los Alamos National Laboratory Electromagnetic Isotope Separator Project. The Los Alamos National Laboratory electromagnetic isotope separator (EMIS) utilizes a Freeman ion source to generate beams of various elements which are accelerated to 40 keV and passed through a 75-degree bend using a large dipole magnet with a radius of 1.2 m. The isotope mass differences translate directly to a spread in momentum, dp, relative to the design momentum p0. Momentum spread is converted to spread in the horizontal arrival location dx at a target chamber by the dispersion of the dipole magnet: dx = D(s)dp/p0. By placing a thin slit leading to a collection chamber at a location xc specific isotope mass is isolated by adjusting the dipole magnet strength or the beam energy. The arriving beam current at xc is associated with average isotope atomic mass, giving an isotope mass spectrum I(m) measured in mA. Although the EMIS is a compact system (5 m) setting up and automatically running at an optimal isotope separation profile I(m) profile is challenging due to time-variation of the complex source as well as un-modeled disturbances. We present preliminary results of developing adaptive machine learning-based tools for the EMIS beam and for the accelerator components.
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA65
About •	Received ※ 18 July 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 10 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEYE2	Upgrade of the FRIB ReAccelerator	cryomodule, experiment, cavity, MMI	572
	A.C.C. Villari, B. Arend, G. Bollen, D.B. Crisp, K.D. Davidson, K. Fukushima, A.I. Henriques, K. Holland, S.H. Kim, A. Lapierre, Y. Liu, T. Maruta, D.G. Morris, S. Nash, P.N. Ostroumov, A.S. Plastun, J. Priller, S. Schwarz, B.M. Sherrill, M. Steiner, C. Sumithrarachchi, R. Walker, T. Zhang, Q. Zhao FRIB, East Lansing, Michigan, USA
	Funding: Work supported by the NSF under grant PHY15-65546 and DOE-SC under award number DE-SC0000661 The reaccelerator facility at FRIB was upgraded to provide new science opportunities. The upgrade included a new ion source to produce stable and long livied rare isotopes in a batch mode, a new room-temperature rebuncher, a new β = 0.085 quarter-wave-resonator cryomodule to increase the beam energy from 3 MeV/u to 6 MeV/u for ions with a charge-to-mass ratio of 1/4, and a new experimental vault with beamlines.
	Slides WEYE2 [4.220 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEYE2
About •	Received ※ 13 July 2022 — Revised ※ 01 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 10 August 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPA44

Utilizing Python to Prepare the VENUS Ion Source for Machine Learning

controls, PLC, interface, ECR

151

A. Kireeff, L. Phair, M.J. Regis, M. Salathe, D.S. Todd
LBNL, Berkeley, California, USA

Funding: This work was supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC02-05CH11231.
The fully superconducting electron cyclotron resonance (ECR) ion source VENUS is one of the world’s two highest-performing ECR ion sources, and a copy of this source will soon be used to produce ion beams at FRIB. The tuning and optimization of ECR ion sources is time consuming, and there are few detailed theoretical models to guide this work. To aid in this process, we are working toward utilizing machine learning to both efficiently optimize VENUS and reliably maintain its stability for long campaigns. We have created a Python library to interface with the programmable logic controller (PLC) in order to operate VENUS and collect and store source and beam data. We will discuss the design and safety considerations that went into creating this library, the implementation of the library, and some of the capabilities it enables.

Poster MOPA44 [0.862 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA44

About •

Received ※ 17 July 2022 — Revised ※ 27 July 2022 — Accepted ※ 05 August 2022 — Issue date ※ 16 August 2022

Cite •

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

TUPA05

An H⁻ Injector for the ESS Storage Ring

cathode, rfq, plasma, operation

357

V.G. Dudnikov, M.A. Cummings, M. Popovic
Muons, Inc, Illinois, USA

H⁻ charge exchange (stripping) injection into the European Spallation neutron Source (ESS) Storage Ring requires a 90 mA H⁻ ion source that delivers 2.9 ms pulses at 14 Hz repetition rate (duty factor ~4%) that can be extended to 28 Hz (df 8%). This can be achieved with a magnetron surface plasma H⁻ source (SPS) with active cathode and anode cooling. The Brookhaven National Laboratory (BNL) magnetron SPS can produce an H⁻ beam current of 100 mA with about 2 kW discharge power and can operate up to 0.7 % duty factor (average power 14 W) without active cooling. We describe how active cathode and anode cooling can be applied to the BNL source to increase the average discharge power up to 140 W (df 8%) to satisfy the needs of the ESS. We also describe the use of a short electrostatic LEBT as is used at the Oak Ridge National Laboratory Spallation Neutron Source to improve the beam delivery to the RFQ.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA05

About •

Received ※ 02 August 2022 — Revised ※ 08 August 2022 — Accepted ※ 10 August 2022 — Issue date ※ 04 September 2022

Cite •

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

TUPA65

Machine Learning for the LANL Electromagnetic Isotope Separator

controls, dipole, feedback, electron

490

A. Scheinker, K.W. Dudeck, C.P. Leibman
LANL, Los Alamos, New Mexico, USA

Funding: Los Alamos National Laboratory Electromagnetic Isotope Separator Project.
The Los Alamos National Laboratory electromagnetic isotope separator (EMIS) utilizes a Freeman ion source to generate beams of various elements which are accelerated to 40 keV and passed through a 75-degree bend using a large dipole magnet with a radius of 1.2 m. The isotope mass differences translate directly to a spread in momentum, dp, relative to the design momentum p0. Momentum spread is converted to spread in the horizontal arrival location dx at a target chamber by the dispersion of the dipole magnet: dx = D(s)dp/p0. By placing a thin slit leading to a collection chamber at a location xc specific isotope mass is isolated by adjusting the dipole magnet strength or the beam energy. The arriving beam current at xc is associated with average isotope atomic mass, giving an isotope mass spectrum I(m) measured in mA. Although the EMIS is a compact system (5 m) setting up and automatically running at an optimal isotope separation profile I(m) profile is challenging due to time-variation of the complex source as well as un-modeled disturbances. We present preliminary results of developing adaptive machine learning-based tools for the EMIS beam and for the accelerator components.

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-TUPA65

About •

Received ※ 18 July 2022 — Revised ※ 07 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 10 August 2022

Cite •

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

WEYE2

Upgrade of the FRIB ReAccelerator

cryomodule, experiment, cavity, MMI

572

A.C.C. Villari, B. Arend, G. Bollen, D.B. Crisp, K.D. Davidson, K. Fukushima, A.I. Henriques, K. Holland, S.H. Kim, A. Lapierre, Y. Liu, T. Maruta, D.G. Morris, S. Nash, P.N. Ostroumov, A.S. Plastun, J. Priller, S. Schwarz, B.M. Sherrill, M. Steiner, C. Sumithrarachchi, R. Walker, T. Zhang, Q. Zhao
FRIB, East Lansing, Michigan, USA

Funding: Work supported by the NSF under grant PHY15-65546 and DOE-SC under award number DE-SC0000661
The reaccelerator facility at FRIB was upgraded to provide new science opportunities. The upgrade included a new ion source to produce stable and long livied rare isotopes in a batch mode, a new room-temperature rebuncher, a new β = 0.085 quarter-wave-resonator cryomodule to increase the beam energy from 3 MeV/u to 6 MeV/u for ions with a charge-to-mass ratio of 1/4, and a new experimental vault with beamlines.

Slides WEYE2 [4.220 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-WEYE2

About •

Received ※ 13 July 2022 — Revised ※ 01 August 2022 — Accepted ※ 08 August 2022 — Issue date ※ 10 August 2022

Cite •

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