IPAC2021 - List of Keywords (radio-frequency)

Paper	Title	Other Keywords	Page
MOPAB372	KARVE: A Nanoparticle Accelerator for Space Thruster Applications	acceleration, ECR, bunching, simulation	1151
	J.W. Lewellen, L.R. Danielson, A. Essunfeld, J.A. Hollingsworth, M.A. Holloway LANL, Los Alamos, New Mexico, USA E.K. Lewis NASA Johnson Space Center, Houston, Texas, USA
	We present a concept for using RF-based acceleration of nanoparticles (NPs) as a means of generating thrust for future space missions: the Kinetic Acceleration & Resource Vector Engine (KARVE) thruster. Acceleration of nanoparticles (NPs) via DC accelerators has been shown to be feasible in dust accelerator labs such as the Heidelberg dust accelerator and the 3 MV hypervelocity dust accelerator at the Colorado Center for Lunar Dust and Atmospheric Studies. In contrast, KARVE uses RF-driven acceleration of nanoparticles as the basis of a thruster design lying between chemical and ion engines in performance: more efficient than chemical engines in terms of specific impulse; and higher thrust than ion engines. The properties of multi-gap RF accelerators also allow an on-the-fly tradeoff between specific impulse and thrust.
	Poster MOPAB372 [0.694 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB372
About •	paper received ※ 19 May 2021 paper accepted ※ 27 May 2021 issue date ※ 10 August 2021
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MOPAB376	Design and Fabrication of a Quadrupole Resonator for SRF R&D	SRF, cavity, quadrupole, niobium	1158
	R. Monroy-Villa, W. Hillert, M. Wenskat University of Hamburg, Institut für Experimentalphysik, Hamburg, Germany S. Gorgi Zadeh, P. Putek Rostock University, Faculty of Computer Science and Electrical Engineering, Rostock, Germany R. Monroy-Villa, D. Reschke, J.H. Thie DESY, Hamburg, Germany
	As Nb superconducting radio-frequency (SRF) cavities are now approaching the theoretical limits of the material, a variety of different surface treatments have been developed to further improve their performance; although no fully understood theory is yet available. Small superconducting samples are studied to characterize their material properties and their evolution under different surface treatments. To study the RF properties of such samples under realistic SRF conditions at low temperatures, a test cavity called quadrupole resonator (QPR) is currently being fabricated. In this work we report the status of the QPR at Universität Hamburg in collaboration with DESY. Our device is based on the QPRs operated at CERN and at HZB and its design will allow for testing samples under cavity-like conditions, i.e., at temperatures between 2K and 8 K, under magnetic fields up to 120mT and with operating frequencies of 433 MHz, 866 MHz and 1300 MHz. Fabrication tolerance studies on the electromagnetic field distributions and simulations of the static detuning of the device, together with a status report on the current manufacturing process, will be presented.
	Poster MOPAB376 [1.119 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB376
About •	paper received ※ 26 May 2021 paper accepted ※ 09 June 2021 issue date ※ 17 August 2021
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TUPAB237	Symplectic Tracking Through Field Maps	quadrupole, cavity, dipole, ion-source	1992
	S.D. Webb RadiaSoft LLC, Boulder, Colorado, USA B.T. Folsom, E. Laface, R. Miyamoto ESS, Lund, Sweden
	For many applications, it is necessary to track particles using field maps, instead of an analytic representation of the fields which is typically not available. These field maps come about while designing elements such as realistic magnets or radiofrequency cavities, and represent the field geometry on a mesh in space. However, simple interpolation of the fields from the field maps does not guarantee that the resulting tracking scheme satisfies the symplectic condition. Here we present a general method to decompose the field-map potential in the sum of interpolating functions that produces, by construction, a symplectic integrator.
	Poster TUPAB237 [0.307 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB237
About •	paper received ※ 19 May 2021 paper accepted ※ 22 July 2021 issue date ※ 22 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THXC05	Simulation of Imaging Using Accelerated Muon Beams	acceleration, simulation, linac, scattering	3740
	M. Otani KEK, Tokai, Ibaraki, Japan H.M. Miyadera LANL, Los Alamos, New Mexico, USA T. Shiba Japan Atomic Energy Agency, Tokai-Mura, Naka-Gun, Ibaraki-Ken, Japan
	Muons are elementary particles with strong penetrating power and cosmic-ray muons have been utilized to see through large structures such as the pyramids. Recently, we have succeeded in accelerating muons using a radio-frequency accelerator, opening the door to new imaging techniques using accelerated muon beams. Currently, imaging with cosmic-ray muons is limited in imaging time and resolution by their intensity and energy fluctuations. The muon beams can have high intensity and monochromatic energy, allowing for better resolution imaging in less time. In this poster, imaging of spent nuclear fuel in casks using cosmic rays and muon beams, as well as imaging in other cases, will be evaluated and compared.
	Poster THXC05 [2.560 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THXC05
About •	paper received ※ 16 May 2021 paper accepted ※ 19 July 2021 issue date ※ 15 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

FRXC01	Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory	cavity, cryomodule, network, SRF	4535
	C. Tennant, A. Carpenter, T. Powers, L.S. Vidyaratne JLab, Newport News, Virginia, USA K.M. Iftekharuddin, M. Rahman ODU, Norfolk, Virginia, USA A.D. Shabalina STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
	Funding: This work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177. We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-FRXC01
About •	paper received ※ 16 May 2021 paper accepted ※ 01 July 2021 issue date ※ 13 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPAB372

KARVE: A Nanoparticle Accelerator for Space Thruster Applications

acceleration, ECR, bunching, simulation

1151

J.W. Lewellen, L.R. Danielson, A. Essunfeld, J.A. Hollingsworth, M.A. Holloway
LANL, Los Alamos, New Mexico, USA
E.K. Lewis
NASA Johnson Space Center, Houston, Texas, USA

We present a concept for using RF-based acceleration of nanoparticles (NPs) as a means of generating thrust for future space missions: the Kinetic Acceleration & Resource Vector Engine (KARVE) thruster. Acceleration of nanoparticles (NPs) via DC accelerators has been shown to be feasible in dust accelerator labs such as the Heidelberg dust accelerator and the 3 MV hypervelocity dust accelerator at the Colorado Center for Lunar Dust and Atmospheric Studies. In contrast, KARVE uses RF-driven acceleration of nanoparticles as the basis of a thruster design lying between chemical and ion engines in performance: more efficient than chemical engines in terms of specific impulse; and higher thrust than ion engines. The properties of multi-gap RF accelerators also allow an on-the-fly tradeoff between specific impulse and thrust.

Poster MOPAB372 [0.694 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB372

About •

paper received ※ 19 May 2021 paper accepted ※ 27 May 2021 issue date ※ 10 August 2021

Export •

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

MOPAB376

Design and Fabrication of a Quadrupole Resonator for SRF R&D

SRF, cavity, quadrupole, niobium

1158

R. Monroy-Villa, W. Hillert, M. Wenskat
University of Hamburg, Institut für Experimentalphysik, Hamburg, Germany
S. Gorgi Zadeh, P. Putek
Rostock University, Faculty of Computer Science and Electrical Engineering, Rostock, Germany
R. Monroy-Villa, D. Reschke, J.H. Thie
DESY, Hamburg, Germany

As Nb superconducting radio-frequency (SRF) cavities are now approaching the theoretical limits of the material, a variety of different surface treatments have been developed to further improve their performance; although no fully understood theory is yet available. Small superconducting samples are studied to characterize their material properties and their evolution under different surface treatments. To study the RF properties of such samples under realistic SRF conditions at low temperatures, a test cavity called quadrupole resonator (QPR) is currently being fabricated. In this work we report the status of the QPR at Universität Hamburg in collaboration with DESY. Our device is based on the QPRs operated at CERN and at HZB and its design will allow for testing samples under cavity-like conditions, i.e., at temperatures between 2K and 8 K, under magnetic fields up to 120mT and with operating frequencies of 433 MHz, 866 MHz and 1300 MHz. Fabrication tolerance studies on the electromagnetic field distributions and simulations of the static detuning of the device, together with a status report on the current manufacturing process, will be presented.

Poster MOPAB376 [1.119 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB376

About •

paper received ※ 26 May 2021 paper accepted ※ 09 June 2021 issue date ※ 17 August 2021

Export •

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

TUPAB237

Symplectic Tracking Through Field Maps

quadrupole, cavity, dipole, ion-source

1992

S.D. Webb
RadiaSoft LLC, Boulder, Colorado, USA
B.T. Folsom, E. Laface, R. Miyamoto
ESS, Lund, Sweden

For many applications, it is necessary to track particles using field maps, instead of an analytic representation of the fields which is typically not available. These field maps come about while designing elements such as realistic magnets or radiofrequency cavities, and represent the field geometry on a mesh in space. However, simple interpolation of the fields from the field maps does not guarantee that the resulting tracking scheme satisfies the symplectic condition. Here we present a general method to decompose the field-map potential in the sum of interpolating functions that produces, by construction, a symplectic integrator.

Poster TUPAB237 [0.307 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB237

About •

paper received ※ 19 May 2021 paper accepted ※ 22 July 2021 issue date ※ 22 August 2021

Export •

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

THXC05

Simulation of Imaging Using Accelerated Muon Beams

acceleration, simulation, linac, scattering

3740

M. Otani
KEK, Tokai, Ibaraki, Japan
H.M. Miyadera
LANL, Los Alamos, New Mexico, USA
T. Shiba
Japan Atomic Energy Agency, Tokai-Mura, Naka-Gun, Ibaraki-Ken, Japan

Muons are elementary particles with strong penetrating power and cosmic-ray muons have been utilized to see through large structures such as the pyramids. Recently, we have succeeded in accelerating muons using a radio-frequency accelerator, opening the door to new imaging techniques using accelerated muon beams. Currently, imaging with cosmic-ray muons is limited in imaging time and resolution by their intensity and energy fluctuations. The muon beams can have high intensity and monochromatic energy, allowing for better resolution imaging in less time. In this poster, imaging of spent nuclear fuel in casks using cosmic rays and muon beams, as well as imaging in other cases, will be evaluated and compared.

Poster THXC05 [2.560 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THXC05

About •

paper received ※ 16 May 2021 paper accepted ※ 19 July 2021 issue date ※ 15 August 2021

Export •

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

FRXC01

Superconducting Radio-Frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

cavity, cryomodule, network, SRF

4535

C. Tennant, A. Carpenter, T. Powers, L.S. Vidyaratne
JLab, Newport News, Virginia, USA
K.M. Iftekharuddin, M. Rahman
ODU, Norfolk, Virginia, USA
A.D. Shabalina
STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom

Funding: This work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Contract No. DE-AC05-06OR23177.
We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-FRXC01

About •

paper received ※ 16 May 2021 paper accepted ※ 01 July 2021 issue date ※ 13 August 2021

Export •

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