IPAC2021 - List of Authors (Huang, X.)

Paper	Title	Page
MOPAB077	Anomaly Detection in Accelerator Facilities Using Machine Learning	304
	A. Das Stanford University, Stanford, California, USA M. Borland, L. Emery, X. Huang, H. Shang, G. Shen ANL, Lemont, Illinois, USA D.F. Ratner SLAC, Menlo Park, California, USA R.M. Smith, G.M. Wang BNL, Upton, New York, USA
	Synchrotron light sources are user facilities and usually run about 5000 hours per year to support many beamlines operations in parallel. Reliability is a key parameter to evaluate machine performance. Even many facilities have achieved >95% beam reliability, there are still many hours of unscheduled downtime and every hour lost is a waste of operation costs along with a big impact on individual scheduled user experiments. Preventive maintenance on subsystems and quick recovery from machine trips are the basic strategies to achieve high reliability, which heavily depends on experts’ dedication. Recently, SLAC, APS, and NSLS-II collaborated to develop machine-learning-based approaches aiming to solve both situations, hardware failure prediction and machine failure diagnosis to find the root sources. In this paper, we report our facility operation status, development progress, and plans.
	Poster MOPAB077 [1.240 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB077
About •	paper received ※ 16 May 2021 paper accepted ※ 14 June 2021 issue date ※ 01 September 2021
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MOPAB213	Characterization of Linear Optics and Beam Parameters for the APS Booster with Turn-by-Turn BPM Data	703
	X. Huang, H. Shang, C. Yao ANL, Lemont, Illinois, USA
	We take turn-by-turn (TBT) BPM data on the energy ramp of the APS Booster, and analyze the data with the independent component analysis. The extraction kicker was used to excite the betatron motion. The linear optics of the machine is characterized with the TBT BPM data. We also analyze the decoherence pattern of the kicked beam, from which we are able to derive beam distribution parameters, such as the momentum spread.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB213
About •	paper received ※ 13 May 2021 paper accepted ※ 11 June 2021 issue date ※ 19 August 2021
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MOPAB214	Linear Optics Measurement for the APS Ring with Turn-by-Turn BPM Data	707
	X. Huang, V. Sajaev, Y.P. Sun, A. Xiao ANL, Lemont, Illinois, USA
	We measure the linear optics of the APS storage ring from turn-by-turn BPM data taken when the beam is excited with an injection kicker. Decoherence due to chromaticity and amplitude-dependent detuning is observed and compared to theoretic predictions. Independent component analysis is used to analyze the data, which separates the betatron normal modes and synchrotron motion, despite contamination of bad BPMs. The beta functions and phase advances are subsequently obtained. The method is used to study the linear optics perturbation of an insertion device.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB214
About •	paper received ※ 12 May 2021 paper accepted ※ 09 June 2021 issue date ※ 01 September 2021
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TUPAB286	Experience with On-line Optimizers for APS Linac Front End Optimization	2151
	H. Shang, M. Borland, X. Huang, Y. Sun ANL, Lemont, Illinois, USA M. Song, Z. Zhang SLAC, Menlo Park, California, USA
	Funding: * Work supported by U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357 and BES R&D project FWP 2020-ANL-34573 While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front end beam dynamics has been difficult to model. One of the issues is that beam properties from the thermionic gun can vary from time to time. As a result, linac front end beam tuning is required to establish good matching and maximize the charge transported through the linac. We have been using a traditional simplex optimizer to find the best settings for the gun front end magnets and steering magnets. However, it takes a long time and requires some fair initial conditions. Therefore, we imported other on-line optimizers, such as robust conjugate direction search (RCDS) which is a classic optimizer as simplex, multi-objective particle swarm (MOPSO), and multi-generation gaussian process optimizer (MG-GPO) which is based on machine learning technique. In this paper we report our experience with these on-line optimizers for maximum bunch charge transportation efficiency through the linac.
	Poster TUPAB286 [2.964 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB286
About •	paper received ※ 12 May 2021 paper accepted ※ 08 July 2021 issue date ※ 29 August 2021
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THPAB082	Recent Operational Experience with Thermionic RF Guns at the APS	3959
	Y. Sun, M. Borland, G.I. Fystro, X. Huang, H. Shang ANL, Lemont, Illinois, USA
	Funding: Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357 The electron beam at the Argonne Advanced Photon Source (APS) is generated from an S-band thermionic RF gun. There are two locations at the frontend of the linac where thermionic RF guns are installed – RG1 and RG2. Three so-called generation-III guns are available, two are installed at RG1 and RG2, one is a spare. In recent years, these guns are showing signs of aging after over a couple of decades of operations. RF trips started to occur, and we had to reduce the nominal operating rf power to alleviate the problem. In addition, beam generated by RG1 suffers from low transportation efficiency from the gun to the linac, and beam trajectory is unstable which results in charge instabilities. Recently, APS obtained a new type of prototype gun and it was beam commissioned in the linac. In this paper, we report our operational experience with these thermionic rf guns including thermionic-cathode beam extraction, gun front-end optimization for maximum charge transmission through the linac, linac lattice setup to match beam for injection into the Particle Accumulator Ring (PAR) and optimization for maximum PAR injection efficiency.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB082
About •	paper received ※ 19 May 2021 paper accepted ※ 28 July 2021 issue date ※ 26 August 2021
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WEPAB304	Multi-Objective Multi-Generation Gaussian Process Optimizer	3383
	X. Huang, M. Song, Z. Zhang 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. We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other recent preselection-assisted algorithms.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB304
About •	paper received ※ 17 May 2021 paper accepted ※ 12 July 2021 issue date ※ 28 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB305	Teeport: Break the Wall Between the Optimization Algorithms and Problems	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
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Anomaly Detection in Accelerator Facilities Using Machine Learning

304

A. Das
Stanford University, Stanford, California, USA
M. Borland, L. Emery, X. Huang, H. Shang, G. Shen
ANL, Lemont, Illinois, USA
D.F. Ratner
SLAC, Menlo Park, California, USA
R.M. Smith, G.M. Wang
BNL, Upton, New York, USA

Synchrotron light sources are user facilities and usually run about 5000 hours per year to support many beamlines operations in parallel. Reliability is a key parameter to evaluate machine performance. Even many facilities have achieved >95% beam reliability, there are still many hours of unscheduled downtime and every hour lost is a waste of operation costs along with a big impact on individual scheduled user experiments. Preventive maintenance on subsystems and quick recovery from machine trips are the basic strategies to achieve high reliability, which heavily depends on experts’ dedication. Recently, SLAC, APS, and NSLS-II collaborated to develop machine-learning-based approaches aiming to solve both situations, hardware failure prediction and machine failure diagnosis to find the root sources. In this paper, we report our facility operation status, development progress, and plans.

Poster MOPAB077 [1.240 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB077

About •

paper received ※ 16 May 2021 paper accepted ※ 14 June 2021 issue date ※ 01 September 2021

Export •

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

MOPAB213

Characterization of Linear Optics and Beam Parameters for the APS Booster with Turn-by-Turn BPM Data

703

X. Huang, H. Shang, C. Yao
ANL, Lemont, Illinois, USA

We take turn-by-turn (TBT) BPM data on the energy ramp of the APS Booster, and analyze the data with the independent component analysis. The extraction kicker was used to excite the betatron motion. The linear optics of the machine is characterized with the TBT BPM data. We also analyze the decoherence pattern of the kicked beam, from which we are able to derive beam distribution parameters, such as the momentum spread.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB213

About •

paper received ※ 13 May 2021 paper accepted ※ 11 June 2021 issue date ※ 19 August 2021

Export •

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

MOPAB214

Linear Optics Measurement for the APS Ring with Turn-by-Turn BPM Data

707

X. Huang, V. Sajaev, Y.P. Sun, A. Xiao
ANL, Lemont, Illinois, USA

We measure the linear optics of the APS storage ring from turn-by-turn BPM data taken when the beam is excited with an injection kicker. Decoherence due to chromaticity and amplitude-dependent detuning is observed and compared to theoretic predictions. Independent component analysis is used to analyze the data, which separates the betatron normal modes and synchrotron motion, despite contamination of bad BPMs. The beta functions and phase advances are subsequently obtained. The method is used to study the linear optics perturbation of an insertion device.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB214

About •

paper received ※ 12 May 2021 paper accepted ※ 09 June 2021 issue date ※ 01 September 2021

Export •

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

TUPAB286

Experience with On-line Optimizers for APS Linac Front End Optimization

2151

H. Shang, M. Borland, X. Huang, Y. Sun
ANL, Lemont, Illinois, USA
M. Song, Z. Zhang
SLAC, Menlo Park, California, USA

Funding: * Work supported by U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357 and BES R&D project FWP 2020-ANL-34573
While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front end beam dynamics has been difficult to model. One of the issues is that beam properties from the thermionic gun can vary from time to time. As a result, linac front end beam tuning is required to establish good matching and maximize the charge transported through the linac. We have been using a traditional simplex optimizer to find the best settings for the gun front end magnets and steering magnets. However, it takes a long time and requires some fair initial conditions. Therefore, we imported other on-line optimizers, such as robust conjugate direction search (RCDS) which is a classic optimizer as simplex, multi-objective particle swarm (MOPSO), and multi-generation gaussian process optimizer (MG-GPO) which is based on machine learning technique. In this paper we report our experience with these on-line optimizers for maximum bunch charge transportation efficiency through the linac.

Poster TUPAB286 [2.964 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB286

About •

paper received ※ 12 May 2021 paper accepted ※ 08 July 2021 issue date ※ 29 August 2021

Export •

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

THPAB082

Recent Operational Experience with Thermionic RF Guns at the APS

3959

Y. Sun, M. Borland, G.I. Fystro, X. Huang, H. Shang
ANL, Lemont, Illinois, USA

Funding: Work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357
The electron beam at the Argonne Advanced Photon Source (APS) is generated from an S-band thermionic RF gun. There are two locations at the frontend of the linac where thermionic RF guns are installed – RG1 and RG2. Three so-called generation-III guns are available, two are installed at RG1 and RG2, one is a spare. In recent years, these guns are showing signs of aging after over a couple of decades of operations. RF trips started to occur, and we had to reduce the nominal operating rf power to alleviate the problem. In addition, beam generated by RG1 suffers from low transportation efficiency from the gun to the linac, and beam trajectory is unstable which results in charge instabilities. Recently, APS obtained a new type of prototype gun and it was beam commissioned in the linac. In this paper, we report our operational experience with these thermionic rf guns including thermionic-cathode beam extraction, gun front-end optimization for maximum charge transmission through the linac, linac lattice setup to match beam for injection into the Particle Accumulator Ring (PAR) and optimization for maximum PAR injection efficiency.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB082

About •

paper received ※ 19 May 2021 paper accepted ※ 28 July 2021 issue date ※ 26 August 2021

Export •

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

WEPAB304

Multi-Objective Multi-Generation Gaussian Process Optimizer

3383

X. Huang, M. Song, Z. Zhang
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.
We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure. In each generation, a surrogate model is constructed for each objective function with the sample data. The models are used to evaluate solutions and to select the ones with a high potential before they are evaluated on the actual system. Since the trial solutions selected by the GP models tend to have better performance than other methods that only rely on random operations, the new algorithm has much higher efficiency in exploring the parameter space. Simulations with multiple test cases show that the new algorithm has a substantially higher convergence speed and stability than NSGA-II, MOPSO, and some other recent preselection-assisted algorithms.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB304

About •

paper received ※ 17 May 2021 paper accepted ※ 12 July 2021 issue date ※ 28 August 2021

Export •

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

WEPAB305

Teeport: Break the Wall Between the Optimization Algorithms and Problems

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

Export •

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