IPAC2021 - List of Keywords (real-time)

Paper	Title	Other Keywords	Page
MOPAB269	Three Approaches for Complete Measurement of the Transverse Beam Optics Along the Fermilab Muon Campus Extraction Line	quadrupole, extraction, dipole, optics	854
	B.D. Simons, M.J. Syphers Northern Illinois University, DeKalb, Illinois, USA D. Stratakis, M.J. Syphers Fermilab, Batavia, Illinois, USA
	Funding: This work was supported through grant DE-SC0020379 with the United States Department of Energy. Traditionally, the process of measuring the optical parameters of a beamline has employed the use of one of two standard methods, namely the three-screen method or a quadrupole magnet scan. Both require either an area of zero dispersion to perform the measurements or knowledge of the dispersion function and momentum spread beforehand in order to provide accurate results. There is however a third method that can be used to measure the standard optical parameters, the beam parameters, the dispersion function, and the momentum spread simultaneously. This method, aptly named the six-screen method, is an extension of the more standard three-screen method. Utilizing the simulation environment of G4beamline, we simulated the 8 GeV proton beam in the M4 beamline and measured the optical and beam parameters using the two standard approaches. Those results were then used as a reference to check the viability of employing the less standard six-screen method in the M4 line. If shown to be a viable option, the six-screen method could be used to retrieve the dispersion function and momentum spread of the beam without needing to change the energy of the beam.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB269
About •	paper received ※ 20 May 2021 paper accepted ※ 07 June 2021 issue date ※ 12 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	electron, network, experiment, laser	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
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MOPAB288	Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler	network, operation, distributed, FPGA	912
	K.J. Hazelwood, M.R. Austin, M.A. Ibrahim, V.P. Nagaslaev, A. Narayanan, D.J. Nicklaus, A.L. Saewert, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran Fermilab, Batavia, Illinois, USA H. Liu, S. Memik, R. Shi, M. Thieme Northwestern University, Evanston, Illinois, USA A. Narayanan Northern Illinois University, DeKalb, Illinois, USA
	The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB288
About •	paper received ※ 19 May 2021 paper accepted ※ 29 July 2021 issue date ※ 13 August 2021
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TUPAB315	Development of Disaster Prevention System for Accelerator Tunnel	radiation, operation, network, neutron	2228
	K. Ishii, K. Bessho, M. Yoshioka KEK, Ibaraki, Japan Y. Kawabata, H. Matsuda, K. Matsumoto Tobishima Corp., Tokyo, Japan S. Tagashira Kansai University, Osaka, Japan N. Yamamoto J-PARC, KEK & JAEA, Ibaraki-ken, Japan
	Funding: This work is supported by Health Labor Sciences Research Grant of Japan In an enclosed space such as a particle accelerator tunnel, ensuring worker safety during a disaster is an issue of critical importance. It is necessary to have a system in which the manager can know from outside the tunnel whether there is any worker left behind and whether the worker is escaping in the right direction. Because a global positioning system (GPS) is not available in the tunnel, we are developing a disaster prevention system that uses Wi-Fi to transmit the positioning of workers and two-way communication. The Wi-Fi access point (AP) installed in the tunnel should be radiation resistant. Additionally, the equipment carried by the worker is convenient and easy to carry. We tested the radiation hardness of commercial AP devices and developed a smartphone application to perform location information transmission and simultaneous character transmission. In 2019, we installed the system on the J-PARC Main Ring and started its operation. In this paper, the functions of the developed system and its prospects are described.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB315
About •	paper received ※ 19 May 2021 paper accepted ※ 10 June 2021 issue date ※ 25 August 2021
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TUPAB353	Remote Commissioning of 400 kW 352 MHz Amplifiers	controls, power-supply, MMI, PLC	2332
	C. Pasotti, A. Cuttin, A. Fabris Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy A. Frizzi, G. Zardi Itelco Broadcast Srl, Orvieto (TR), Italy M. Rossi DB Science, Padova, Italy
	In the framework of the European Spallation Source ERIC (ESS ERIC) In-Kind collaboration, Elettra Sincrotrone Trieste has the task to deliver 26 400 kW 352 MHz Radio Frequency Power Station (RFPS) units. They will feed the Spoke Cavities section of the proton Linac. The RFPS manufacturing contract has been awarded to the European Science Solutions consortium (ESS-C) gained the. The production of the amplifiers is well underway and it has reached a steady rate of delivery. Each RFPS is subject to a Factory Acceptance Test (FAT). In this contribution, the main results of the FATs are presented, together with the FAT remote session protocol. This protocol has been specifically developed to cope with the traveling and in persons meeting restrictions imposed by the COVID-19 pandemic.
	Poster TUPAB353 [2.675 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB353
About •	paper received ※ 17 May 2021 paper accepted ※ 23 June 2021 issue date ※ 17 August 2021
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WEPAB305	Teeport: Break the Wall Between the Optimization Algorithms and Problems	experiment, controls, monitoring, GUI	3387
	Z. Zhang, X. Huang, M. Song SLAC, Menlo Park, California, USA
	Funding: DOE, Office of Science, Office of Basic Energy Sciences, DE-AC02-76SF00515 and FWP 2018-SLAC-100469 Computing Science, Office of Advanced Scientific Computing Research, FWP 2018-SLAC-100469ASCR. Optimization algorithms/techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and Gaussian process (GP) have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, sometimes even unrealistic, since the algorithms and problems could be implemented in different languages, might require specific resources, or have physical constraints. We introduce an optimization platform named Teeport that is developed to address the above issue. This real-time communication (RTC) based platform is particularly designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB305
About •	paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 27 August 2021
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WEPAB356	Proposal of an Alignment System for HALF: The Reference Network of Alignment	alignment, monitoring, simulation, network	3533
	X. Li, J.X. Chen, X.Y. He, W. Wang, Z.Y. Wang USTC/NSRL, Hefei, Anhui, People’s Republic of China J.X. Chen, T. Luo IHEP CSNS, Guangdong Province, People’s Republic of China
	As a fourth-generation light source based on the diffraction-limited storage ring, Hefei Advanced Light Facility (HALF) has higher requirements for magnets alignment in accuracy, efficiency, and reliability. In this paper, the Reference Network of Alignment (RNA) system is proposed to improve the magnetic axis alignment accuracy on the radial direction of the beamline. Herein, we mainly introduce the concept design and the theoretical analysis of the RNA system, which center on the novel fusion method of sensors. A simulation result shows that it is credible to assume the RNA system can achieve an alignment installation accuracy of 20 µm and a displacement monitoring accuracy of 10 µm.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB356
About •	paper received ※ 16 May 2021 paper accepted ※ 21 June 2021 issue date ※ 31 August 2021
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WEPAB412	Use of a Noise IoT Detection System to Measure the Environmental Noise in Taiwan Light Source	monitoring, network, site, experiment	3671
	P.J. Wen, S.P. Kao, S.Y. Lin, Y.C. Lin NSRRC, Hsinchu, Taiwan
	In the past, the method of general noise monitoring altered little; noise was still measured with a human hand-held mobile device, or the measurement at fixed sites was made using traditional analogue data-storage equipment. In recent years, with the rapidly improved network transmission capabilities, the development of a small noise-detection IoT system allows the detection data to be transmitted wirelessly without need for human strength measurements, and records noise information. The statistics of subsequent noise data become a basis for analysis and improvement. Taiwan Light Source (TLS) beamlines have many vacuum pumps, cooling pumps, liquid-nitrogen pressure-relief systems, computer servers etc. that generate much noise. This study is expected to prepare for installation of noise detection. The system uses a noise-detection box to detect, to disclose louder locations, to collect noise data, to determine the source and type of noise source, and to provide information to reduce the noise of the working environment. The TLS noise-detection results find that the inner-ring area has less noise and are more stable than the outer ring area.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB412
About •	paper received ※ 14 May 2021 paper accepted ※ 24 June 2021 issue date ※ 27 August 2021
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THPAB138	FEbreak: A Comprehensive Diagnostic and Automated Conditioning Interface for Analysis of Breakdown and Dark Current Effects	controls, cavity, FPGA, software	4027
	M.E. Schneider, S.V. Baryshev Michigan State University, East Lansing, Michigan, USA R.L. Fleming, D. Gorelov, J.W. Lewellen, E.I. Simakov LANL, Los Alamos, New Mexico, USA E. Jevarjian MSU, East Lansing, Michigan, USA
	Funding: DE-AC02-06CH11357, No. DE-SC0018362, DE-NA-0003525, DE-AC52-06NA25396, LA-UR-21-20613 As the next generation of accelerator technology pushes towards being able to achieve higher and higher gradients there is a need to develop high-frequency structures that can support these fields . The conditioning process of the structures and waveguides to high gradient is a labor-intensive process, its length increases as the maximum gradient is increased. This results in the need to automate the conditioning process. This automation must allow for high accuracy calculations of the breakdown probabilities associated with the conditioning process which can be used to instruct the conditioning procedure without the need for human intervention. To automate the conditioning process at LANL’s high gradient C-band accelerator test stand we developed FEbreak that is a breakout probability and conditioning automation software that is a part of the FEmaster series , , *. FEbreak directly interfaces with the rest of FEmaster to automate the data collection and data processing to not only analyze the breakdown probability but also the dark current effects associated with these high gradient structures. E. I. Simakov Nuc. Inst. and Meth, in Phy. Research Section A: Acc. Spec, 907 221 (2019) E. Jevarjian arXiv:2009.13046 * T. Y. Posos arXiv:2012.03578 **** M. Schneider arXiv:2012.10804
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB138
About •	paper received ※ 18 May 2021 paper accepted ※ 02 July 2021 issue date ※ 16 August 2021
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THPAB252	Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators	operation, klystron, controls, high-voltage	4303
	G.C. Pappas ORNL RAD, Oak Ridge, Tennessee, USA D. Lu ORNL, Oak Ridge, Tennessee, USA M. Schram JLab, Newport News, Virginia, USA D.L. Vrabie PNNL, Richland, Washington, USA
	Funding: SNS/ORNL is managed by UT-Battelle, LLC, under contract DE-AC05-00OR22725 for the U.S. Department of Energy Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms. * G. Dodson, Approach to Reliable Operations, 26-Dodson_Approach to Reliable Operation-r1.pdf, Feb., 2010.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB252
About •	paper received ※ 18 May 2021 paper accepted ※ 14 July 2021 issue date ※ 29 August 2021
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Paper

Title

Other Keywords

Page

MOPAB269

Three Approaches for Complete Measurement of the Transverse Beam Optics Along the Fermilab Muon Campus Extraction Line

quadrupole, extraction, dipole, optics

854

B.D. Simons, M.J. Syphers
Northern Illinois University, DeKalb, Illinois, USA
D. Stratakis, M.J. Syphers
Fermilab, Batavia, Illinois, USA

Funding: This work was supported through grant DE-SC0020379 with the United States Department of Energy.
Traditionally, the process of measuring the optical parameters of a beamline has employed the use of one of two standard methods, namely the three-screen method or a quadrupole magnet scan. Both require either an area of zero dispersion to perform the measurements or knowledge of the dispersion function and momentum spread beforehand in order to provide accurate results. There is however a third method that can be used to measure the standard optical parameters, the beam parameters, the dispersion function, and the momentum spread simultaneously. This method, aptly named the six-screen method, is an extension of the more standard three-screen method. Utilizing the simulation environment of G4beamline, we simulated the 8 GeV proton beam in the M4 beamline and measured the optical and beam parameters using the two standard approaches. Those results were then used as a reference to check the viability of employing the less standard six-screen method in the M4 line. If shown to be a viable option, the six-screen method could be used to retrieve the dispersion function and momentum spread of the beam without needing to change the energy of the beam.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB269

About •

paper received ※ 20 May 2021 paper accepted ※ 07 June 2021 issue date ※ 12 August 2021

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MOPAB286

Towards a Data Science Enabled MeV Ultrafast Electron Diffraction System

electron, network, experiment, laser

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

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

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MOPAB288

Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler

network, operation, distributed, FPGA

912

K.J. Hazelwood, M.R. Austin, M.A. Ibrahim, V.P. Nagaslaev, A. Narayanan, D.J. Nicklaus, A.L. Saewert, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran
Fermilab, Batavia, Illinois, USA
H. Liu, S. Memik, R. Shi, M. Thieme
Northwestern University, Evanston, Illinois, USA
A. Narayanan
Northern Illinois University, DeKalb, Illinois, USA

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB288

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paper received ※ 19 May 2021 paper accepted ※ 29 July 2021 issue date ※ 13 August 2021

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TUPAB315

Development of Disaster Prevention System for Accelerator Tunnel

radiation, operation, network, neutron

2228

K. Ishii, K. Bessho, M. Yoshioka
KEK, Ibaraki, Japan
Y. Kawabata, H. Matsuda, K. Matsumoto
Tobishima Corp., Tokyo, Japan
S. Tagashira
Kansai University, Osaka, Japan
N. Yamamoto
J-PARC, KEK & JAEA, Ibaraki-ken, Japan

Funding: This work is supported by Health Labor Sciences Research Grant of Japan
In an enclosed space such as a particle accelerator tunnel, ensuring worker safety during a disaster is an issue of critical importance. It is necessary to have a system in which the manager can know from outside the tunnel whether there is any worker left behind and whether the worker is escaping in the right direction. Because a global positioning system (GPS) is not available in the tunnel, we are developing a disaster prevention system that uses Wi-Fi to transmit the positioning of workers and two-way communication. The Wi-Fi access point (AP) installed in the tunnel should be radiation resistant. Additionally, the equipment carried by the worker is convenient and easy to carry. We tested the radiation hardness of commercial AP devices and developed a smartphone application to perform location information transmission and simultaneous character transmission. In 2019, we installed the system on the J-PARC Main Ring and started its operation. In this paper, the functions of the developed system and its prospects are described.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB315

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paper received ※ 19 May 2021 paper accepted ※ 10 June 2021 issue date ※ 25 August 2021

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TUPAB353

Remote Commissioning of 400 kW 352 MHz Amplifiers

controls, power-supply, MMI, PLC

2332

C. Pasotti, A. Cuttin, A. Fabris
Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
A. Frizzi, G. Zardi
Itelco Broadcast Srl, Orvieto (TR), Italy
M. Rossi
DB Science, Padova, Italy

In the framework of the European Spallation Source ERIC (ESS ERIC) In-Kind collaboration, Elettra Sincrotrone Trieste has the task to deliver 26 400 kW 352 MHz Radio Frequency Power Station (RFPS) units. They will feed the Spoke Cavities section of the proton Linac. The RFPS manufacturing contract has been awarded to the European Science Solutions consortium (ESS-C) gained the. The production of the amplifiers is well underway and it has reached a steady rate of delivery. Each RFPS is subject to a Factory Acceptance Test (FAT). In this contribution, the main results of the FATs are presented, together with the FAT remote session protocol. This protocol has been specifically developed to cope with the traveling and in persons meeting restrictions imposed by the COVID-19 pandemic.

Poster TUPAB353 [2.675 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB353

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

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WEPAB305

Teeport: Break the Wall Between the Optimization Algorithms and Problems

experiment, controls, monitoring, GUI

3387

Z. Zhang, X. Huang, M. Song
SLAC, Menlo Park, California, USA

Funding: DOE, Office of Science, Office of Basic Energy Sciences, DE-AC02-76SF00515 and FWP 2018-SLAC-100469 Computing Science, Office of Advanced Scientific Computing Research, FWP 2018-SLAC-100469ASCR.
Optimization algorithms/techniques such as genetic algorithm (GA), particle swarm optimization (PSO) and Gaussian process (GP) have been widely used in the accelerator field to tackle complex design/online optimization problems. However, connecting the algorithm with the optimization problem can be difficult, sometimes even unrealistic, since the algorithms and problems could be implemented in different languages, might require specific resources, or have physical constraints. We introduce an optimization platform named Teeport that is developed to address the above issue. This real-time communication (RTC) based platform is particularly designed to minimize the effort of integrating the algorithms and problems. Once integrated, the users are granted a rich feature set, such as monitoring, controlling, and benchmarking. Some real-life applications of the platform are also discussed.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB305

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paper received ※ 20 May 2021 paper accepted ※ 02 July 2021 issue date ※ 27 August 2021

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WEPAB356

Proposal of an Alignment System for HALF: The Reference Network of Alignment

alignment, monitoring, simulation, network

3533

X. Li, J.X. Chen, X.Y. He, W. Wang, Z.Y. Wang
USTC/NSRL, Hefei, Anhui, People’s Republic of China
J.X. Chen, T. Luo
IHEP CSNS, Guangdong Province, People’s Republic of China

As a fourth-generation light source based on the diffraction-limited storage ring, Hefei Advanced Light Facility (HALF) has higher requirements for magnets alignment in accuracy, efficiency, and reliability. In this paper, the Reference Network of Alignment (RNA) system is proposed to improve the magnetic axis alignment accuracy on the radial direction of the beamline. Herein, we mainly introduce the concept design and the theoretical analysis of the RNA system, which center on the novel fusion method of sensors. A simulation result shows that it is credible to assume the RNA system can achieve an alignment installation accuracy of 20 µm and a displacement monitoring accuracy of 10 µm.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB356

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paper received ※ 16 May 2021 paper accepted ※ 21 June 2021 issue date ※ 31 August 2021

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WEPAB412

Use of a Noise IoT Detection System to Measure the Environmental Noise in Taiwan Light Source

monitoring, network, site, experiment

3671

P.J. Wen, S.P. Kao, S.Y. Lin, Y.C. Lin
NSRRC, Hsinchu, Taiwan

In the past, the method of general noise monitoring altered little; noise was still measured with a human hand-held mobile device, or the measurement at fixed sites was made using traditional analogue data-storage equipment. In recent years, with the rapidly improved network transmission capabilities, the development of a small noise-detection IoT system allows the detection data to be transmitted wirelessly without need for human strength measurements, and records noise information. The statistics of subsequent noise data become a basis for analysis and improvement. Taiwan Light Source (TLS) beamlines have many vacuum pumps, cooling pumps, liquid-nitrogen pressure-relief systems, computer servers etc. that generate much noise. This study is expected to prepare for installation of noise detection. The system uses a noise-detection box to detect, to disclose louder locations, to collect noise data, to determine the source and type of noise source, and to provide information to reduce the noise of the working environment. The TLS noise-detection results find that the inner-ring area has less noise and are more stable than the outer ring area.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB412

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paper received ※ 14 May 2021 paper accepted ※ 24 June 2021 issue date ※ 27 August 2021

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THPAB138

FEbreak: A Comprehensive Diagnostic and Automated Conditioning Interface for Analysis of Breakdown and Dark Current Effects

controls, cavity, FPGA, software

4027

M.E. Schneider, S.V. Baryshev
Michigan State University, East Lansing, Michigan, USA
R.L. Fleming, D. Gorelov, J.W. Lewellen, E.I. Simakov
LANL, Los Alamos, New Mexico, USA
E. Jevarjian
MSU, East Lansing, Michigan, USA

Funding: DE-AC02-06CH11357, No. DE-SC0018362, DE-NA-0003525, DE-AC52-06NA25396, LA-UR-21-20613
As the next generation of accelerator technology pushes towards being able to achieve higher and higher gradients there is a need to develop high-frequency structures that can support these fields *. The conditioning process of the structures and waveguides to high gradient is a labor-intensive process, its length increases as the maximum gradient is increased. This results in the need to automate the conditioning process. This automation must allow for high accuracy calculations of the breakdown probabilities associated with the conditioning process which can be used to instruct the conditioning procedure without the need for human intervention. To automate the conditioning process at LANL’s high gradient C-band accelerator test stand we developed FEbreak that is a breakout probability and conditioning automation software that is a part of the FEmaster series **, ***, ****. FEbreak directly interfaces with the rest of FEmaster to automate the data collection and data processing to not only analyze the breakdown probability but also the dark current effects associated with these high gradient structures.
* E. I. Simakov Nuc. Inst. and Meth, in Phy. Research Section A: Acc. Spec, 907 221 (2019)
** E. Jevarjian arXiv:2009.13046
*** T. Y. Posos arXiv:2012.03578
**** M. Schneider arXiv:2012.10804

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB138

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paper received ※ 18 May 2021 paper accepted ※ 02 July 2021 issue date ※ 16 August 2021

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THPAB252

Machine Learning for Improved Availability of the SNS Klystron High Voltage Converter Modulators

operation, klystron, controls, high-voltage

4303

G.C. Pappas
ORNL RAD, Oak Ridge, Tennessee, USA
D. Lu
ORNL, Oak Ridge, Tennessee, USA
M. Schram
JLab, Newport News, Virginia, USA
D.L. Vrabie
PNNL, Richland, Washington, USA

Funding: SNS/ORNL is managed by UT-Battelle, LLC, under contract DE-AC05-00OR22725 for the U.S. Department of Energy
Beam availability has increased at the SNS, however, the targeted availability is greater than 95 %, while the SNS has failed to meet lower targets in the past. The HVCM used to power the linac klystrons have been one source of lost beam time and was chosen to explore using AI/ML techniques to improve reliability. Among the possibilities being explored are automating the tuning of HVCMs and predicting component failures such as capacitor aging, rectifier assemblies containing hundreds of diodes, and insulating oil degradation. The methodology pursued includes data cleaning, de-noising, post-analysis data labeling, and machine learning model development. We explore using Long Short-Term Memory and autoencoders for anomaly detection and prognostication used to schedule maintenance. We evaluate the use of model regularizers and constraints to improve the performance of the model and investigate methods to estimate the uncertainty of the models to provide a robust prediction with statistical interoperability. This paper describes the operational experience and known failures of the HVCMs and the proposed ML methodology and the preliminary results of training the AI/ML algorithms.
* G. Dodson, Approach to Reliable Operations, 26-Dodson_Approach to Reliable Operation-r1.pdf, Feb., 2010.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB252

About •

paper received ※ 18 May 2021 paper accepted ※ 14 July 2021 issue date ※ 29 August 2021

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