IPAC2021 - Classification: MC6: Beam Instrumentation, Controls, Feedback and Operational Aspects / T04 Accelerator/Storage Ring Control Systems

Paper	Title	Page
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)

TUPAB289	Towards Hysteresis Aware Bayesian Regression and Optimization	2159
	R.J. Roussel University of Chicago, Chicago, Illinois, USA A. Hanuka SLAC, Menlo Park, California, USA
	Funding: This work was supported by the U.S. National Science Foundation under Award No. PHY-1549132, the Center for Bright Beams. Algorithms used today for accelerator optimization assume a simple proportional relationship between an intermediate tuning parameter and the resultant field or mechanism which influences the beam. This neglects the effects of hysteresis, where the magnetic or mechanical response depends not only on the current parameter value, but also on the historical parameter values. This prevents the use of one to one surrogate models, such as Gaussian processes, to assist in optimization when hysteresis effects are not negligible, since identical points in input space no longer correspond to a same point in output space. In this work, we demonstrate how Bayesian inference can be used in conjunction with Gaussian processes to jointly model both the hysteresis cycle of magnetic elements and the beam response. Using this technique we demonstrate how to model the hysteresis cycle of a magnet during accelerator operation in situ by only measuring the beam response, without direct magnetic field measurements. This allows us to quickly build accurate statistical models of the beam response that can be used for rapid tuning of accelerators where hysteresis effects are dominant.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB289
About •	paper received ※ 18 May 2021 paper accepted ※ 24 June 2021 issue date ※ 19 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB290	Demonstration of Machine Learning Front-End Optimization of the Advanced Photon Source Linac	2163
	A. Hanuka, J.P. Duris SLAC, Menlo Park, California, USA H. Shang, Y. Sun ANL, Lemont, Illinois, USA
	The electron beam for the Advanced Photon Source (APS) at Argonne National Laboratory is generated from a thermionic RF gun and accelerated by an S-band linear accelerator – the APS linac. While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front-end beam dynamics have been difficult to model. One of the issues is that beam properties from thermionic guns can vary. As a result, linac front-end beam tuning is required to establish good matching and maximize the charge transported through the linac. A traditional Nelder-Mead simplex optimizer has been used to find the best settings for the sixteen quadrupoles and steering magnets. However, it takes a long time and requires some fair initial conditions. The Gaussian Process (GP) optimizer does not have the initial condition limitation and runs several times faster. In this paper, we report our data collection and analysis for the training of the GP hyperparameters and discuss the application of GP optimizer on the APS linac front-end optimization for maximum bunch charge transportation efficiency through the linac.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB290
About •	paper received ※ 09 May 2021 paper accepted ※ 28 July 2021 issue date ※ 27 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB291	Subsystem Level Data Acquisition for the Optical Synchronization System at European XFEL	2167
	M. Schütte, A. Eichler, T. Lamb, V. Rybnikov, H. Schlarb, T. Wilksen DESY, Hamburg, Germany
	The optical synchronization system for the European X-Ray Free-Electron Laser provides sub-10 femtosecond timing precision * for the accelerator subsystems and experiments. This is achieved by phase locking a mode-locked laser oscillator to the main RF reference and distributing the optical pulse train carrying the time information via actively propagation-time stabilized optical fibers to multiple end-stations. Making up roughly one percent of the entire European XFEL, it is the first subsystem to receive a large-scale data acquisition system [2] for storing not just hand-selected information, but in fact all diagnostic, monitoring, and configuration data relevant to the optical synchronization available from the distributed control system infrastructure. A minimum of 100 TB per year may be stored in a persistent archive for long-term health monitoring and data mining whereas excess data is stored in a short-term ring buffer for high-resolution fault analysis and feature extraction algorithm development. This paper describes scale, challenges and first experiences from the optical synchronization data acquisition system. * S. Schulz et al., "Few-Femtosecond Facility-Wide Sync. of the European XFEL," in Proc. FEL’19 ** T. Wilksen et al., "A Bunch-Sync. DAQ System for the European XFEL," in Proc. ICALEPCS’17
	Poster TUPAB291 [0.281 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB291
About •	paper received ※ 14 May 2021 paper accepted ※ 17 June 2021 issue date ※ 24 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB292	Automation of the ReAccelerator Linac Phasing	2170
	D.J. Barofsky, A.I. Henriques, T.J. Kabana, A.S. Plastun FRIB, East Lansing, Michigan, USA D.B. Crisp, A. Lapierre, S. Nash, A.C.C. Villari NSCL, East Lansing, Michigan, USA
	Funding: This work is supported by the National Science Foundation under Grant No. PHY-1565546 The ReAccelerator (ReA) at the National Superconducting Cyclotron Laboratory at Michigan State University is a unique facility, as it offers the possibility to reaccelerate not only stable, but rare-isotope beams produced by fast-projectile fragmentation or fission. At ReA, beams are accelerated using a Radio-Frequency-Quadrupole and a superconducting linear accelerator before being delivered to experiments. Beam preparation time plays a major role in the availability of beams to experiments. One of the major time consuming tasks is the linac phasing, since there are 23 resonator cavities to be phased, usually with very low beam intensities. This procedure was automated using a combination of EPICS (Experimental Physics and Industrial Controls System) In/Output Controllers (IOCs) and IOC triggered scripts to scan the resonator phase delay and measure the change in beam energy. We have developed user-friendly tools to phase the linac, which have been tested, making the task of phasing substantially easier. In this presentation, we will present our methodology, challenges faced, tools developed, and initial results of the application for automating the phasing of the ReA linac.
	Poster TUPAB292 [1.140 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB292
About •	paper received ※ 19 May 2021 paper accepted ※ 02 June 2021 issue date ※ 29 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB295	Upgrade to the EPICS Control System at the Argonne Wakefield Accelerator Test Facility	2173
	W. Liu, J.M. Byrd, D.S. Doran, G. Ha, A.N. Johnson, P. Piot, J.G. Power, J.H. Shao, G. Shen, C. Whiteford, E.E. Wisniewski ANL, Lemont, Illinois, USA
	Funding: US Department of Energy, Office of Science The Argonne Wakefield Accelerator (AWA) Test Facility has used a completely homebrewed, MS Windows-based control system for the last 20 years. In an effort to modernize the control system and prepare for an active machine learning program, the AWA will work with the Advanced Photon Source (APS) controls group to upgrade its control system to EPICS. The EPICS control system is expected to facilitate collaborations and support the future growth of AWA. An overview of the previous AWA control and data acquisition system is presented, along with a vision and path for completing the EPICS upgrade.
	Poster TUPAB295 [1.108 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB295
About •	paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 30 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB297	Data Archive System for Superconducting RIKEN Linear Accelerator at RIBF	2178
	A. Uchiyama, N. Fukunishi, M. Kidera, M. Komiyama RIKEN Nishina Center, Wako, Japan
	At RIKEN Nishina Center, superconducting RIKEN Linear Accelerator (SRILAC) was newly installed at downstream of existing accelerator and upgraded for the search experiments of super-heavy-elements with atomic numbers of 119 and higher. For the data archiving and the data visualization in RI Beam Factory (RIBF) project, we have utilized RIBFCAS (RIBF control archive system) since 2009. For the number of archived data point was expected to increase dramatically for SRILAC, we introduced the Archiver Appliance for improvement of the data archiving performance. On the other hand, to realize a user-friendly system about the data visualization, the data of RIBFCAS and the Archiver Appliance should be visualized on the same system. In this system, by implementing a Web application to convert the RIBFCAS data to JSON format, it became possible to unify the data format with the Archiver Appliance and display the data with the same viewer software. In the SRILAC beam commissioning, it became to useful system for finding anomalies and understanding the behavior of superconducting cavity. In this conference, we report the system implementation, developed tool, and the future plan in detail.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB297
About •	paper received ※ 19 May 2021 paper accepted ※ 10 June 2021 issue date ※ 17 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPAB298	First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT	2182
	A. Eichler, F. Burkart, J. Kaiser, W. Kuropka, O. Stein DESY, Hamburg, Germany E. Bründermann, A. Santamaria Garcia, C. Xu KIT, Karlsruhe, Germany
	Funding: Helmholtz Artificial Cooperation Unit Reinforcement Learning algorithms have risen in popularity in recent years in the accelerator physics community, showing potential in beam control and in the optimization and automation of tasks in accelerator operation. The Helmholtz AI project "Machine Learning toward Autonomous Accelerators" is a collaboration between DESY and KIT that works on investigating and developing RL applications for the automatic start-up of electron linear accelerators. The work is carried out in parallel at two similar research accelerators: ARES at DESY and FLUTE at KIT, giving the unique opportunity of transfer learning between facilities. One of the first steps of this project is the establishment of a common interface between the simulations and the machine, in order to test and apply various optimization approaches interchangeably between the two accelerators. In this paper we present the first results on the common interface and its application to beam focusing in ARES, and the idea of laser shaping with spatial light modulators at FLUTE.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB298
About •	paper received ※ 19 May 2021 paper accepted ※ 02 August 2021 issue date ※ 17 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB323	High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade	3434
	G. Shen, N.D. Arnold, T.G. Berenc, J. Carwardine, E. Chandler, T. Fors, T.J. Madden, D.R. Paskvan, C. Roehrig, S.E. Shoaf, S. Veseli ANL, Lemont, Illinois, USA
	Funding: Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract DE-AC02-06CH11357. It is well known that the efficiency of an advanced control algorithm like machine learning is as good as its data quality. Much recent progress in technology enables the massive data acquisition from a control system of modern particle accelerator, and the wide use of embedded controllers, like field-programmable gate arrays (FPGA), provides an opportunity to collect fast data from technical subsystems for monitoring, statistics, diagnostics or fault recording. To improve the data quality, at the APS Upgrade project, a general-purpose data acquisition (DAQ) system is under active development. The APS-U DAQ system collects high-quality fast data from underneath embedded controllers, especially the FPGAs, with the manner of time-correlation and synchronously sampling, which could be used for commissioning, performance monitoring, troubleshooting, and early fault detection, etc. This paper presents the design and latest progress of APS-U high-performance DAQ infrastructure, as well as its preparation to enable the use of machine learning technology for APS-U, and its use cases at APS.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB323
About •	paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 29 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPAB259	High Level Applications for Sirius Accelerators Control	4314
	A.C.S. Oliveira, M.B. Alves, L. Liu, X.R. Resende, F.H. de Sá LNLS, Campinas, Brazil
	Sirius is a 4th generation 3 GeV synchrotron light source that has just finalised the first commissioning phase at the Brazilian Center for Research in Energy and Materials (CNPEM) campus in Campinas, Brazil. The large number of process variables and large complexity of the subsystems in this type of machine requires the development of tools to simplify the commissioning and operation of the accelerators. This paper describes some of the high level control tools developed for the accelerators commissioning and future operation.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB259
About •	paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 21 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

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)

TUPAB289

Towards Hysteresis Aware Bayesian Regression and Optimization

2159

R.J. Roussel
University of Chicago, Chicago, Illinois, USA
A. Hanuka
SLAC, Menlo Park, California, USA

Funding: This work was supported by the U.S. National Science Foundation under Award No. PHY-1549132, the Center for Bright Beams.
Algorithms used today for accelerator optimization assume a simple proportional relationship between an intermediate tuning parameter and the resultant field or mechanism which influences the beam. This neglects the effects of hysteresis, where the magnetic or mechanical response depends not only on the current parameter value, but also on the historical parameter values. This prevents the use of one to one surrogate models, such as Gaussian processes, to assist in optimization when hysteresis effects are not negligible, since identical points in input space no longer correspond to a same point in output space. In this work, we demonstrate how Bayesian inference can be used in conjunction with Gaussian processes to jointly model both the hysteresis cycle of magnetic elements and the beam response. Using this technique we demonstrate how to model the hysteresis cycle of a magnet during accelerator operation in situ by only measuring the beam response, without direct magnetic field measurements. This allows us to quickly build accurate statistical models of the beam response that can be used for rapid tuning of accelerators where hysteresis effects are dominant.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB289

About •

paper received ※ 18 May 2021 paper accepted ※ 24 June 2021 issue date ※ 19 August 2021

Export •

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

TUPAB290

Demonstration of Machine Learning Front-End Optimization of the Advanced Photon Source Linac

2163

A. Hanuka, J.P. Duris
SLAC, Menlo Park, California, USA
H. Shang, Y. Sun
ANL, Lemont, Illinois, USA

The electron beam for the Advanced Photon Source (APS) at Argonne National Laboratory is generated from a thermionic RF gun and accelerated by an S-band linear accelerator – the APS linac. While the APS linac lattice is set up using a model developed with ELEGANT, the thermionic RF gun front-end beam dynamics have been difficult to model. One of the issues is that beam properties from thermionic guns can vary. As a result, linac front-end beam tuning is required to establish good matching and maximize the charge transported through the linac. A traditional Nelder-Mead simplex optimizer has been used to find the best settings for the sixteen quadrupoles and steering magnets. However, it takes a long time and requires some fair initial conditions. The Gaussian Process (GP) optimizer does not have the initial condition limitation and runs several times faster. In this paper, we report our data collection and analysis for the training of the GP hyperparameters and discuss the application of GP optimizer on the APS linac front-end optimization for maximum bunch charge transportation efficiency through the linac.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB290

About •

paper received ※ 09 May 2021 paper accepted ※ 28 July 2021 issue date ※ 27 August 2021

Export •

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

TUPAB291

Subsystem Level Data Acquisition for the Optical Synchronization System at European XFEL

2167

M. Schütte, A. Eichler, T. Lamb, V. Rybnikov, H. Schlarb, T. Wilksen
DESY, Hamburg, Germany

The optical synchronization system for the European X-Ray Free-Electron Laser provides sub-10 femtosecond timing precision * for the accelerator subsystems and experiments. This is achieved by phase locking a mode-locked laser oscillator to the main RF reference and distributing the optical pulse train carrying the time information via actively propagation-time stabilized optical fibers to multiple end-stations. Making up roughly one percent of the entire European XFEL, it is the first subsystem to receive a large-scale data acquisition system [2] for storing not just hand-selected information, but in fact all diagnostic, monitoring, and configuration data relevant to the optical synchronization available from the distributed control system infrastructure. A minimum of 100 TB per year may be stored in a persistent archive for long-term health monitoring and data mining whereas excess data is stored in a short-term ring buffer for high-resolution fault analysis and feature extraction algorithm development. This paper describes scale, challenges and first experiences from the optical synchronization data acquisition system.
* S. Schulz et al., "Few-Femtosecond Facility-Wide Sync. of the European XFEL," in Proc. FEL’19
** T. Wilksen et al., "A Bunch-Sync. DAQ System for the European XFEL," in Proc. ICALEPCS’17

Poster TUPAB291 [0.281 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB291

About •

paper received ※ 14 May 2021 paper accepted ※ 17 June 2021 issue date ※ 24 August 2021

Export •

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

TUPAB292

Automation of the ReAccelerator Linac Phasing

2170

D.J. Barofsky, A.I. Henriques, T.J. Kabana, A.S. Plastun
FRIB, East Lansing, Michigan, USA
D.B. Crisp, A. Lapierre, S. Nash, A.C.C. Villari
NSCL, East Lansing, Michigan, USA

Funding: This work is supported by the National Science Foundation under Grant No. PHY-1565546
The ReAccelerator (ReA) at the National Superconducting Cyclotron Laboratory at Michigan State University is a unique facility, as it offers the possibility to reaccelerate not only stable, but rare-isotope beams produced by fast-projectile fragmentation or fission. At ReA, beams are accelerated using a Radio-Frequency-Quadrupole and a superconducting linear accelerator before being delivered to experiments. Beam preparation time plays a major role in the availability of beams to experiments. One of the major time consuming tasks is the linac phasing, since there are 23 resonator cavities to be phased, usually with very low beam intensities. This procedure was automated using a combination of EPICS (Experimental Physics and Industrial Controls System) In/Output Controllers (IOCs) and IOC triggered scripts to scan the resonator phase delay and measure the change in beam energy. We have developed user-friendly tools to phase the linac, which have been tested, making the task of phasing substantially easier. In this presentation, we will present our methodology, challenges faced, tools developed, and initial results of the application for automating the phasing of the ReA linac.

Poster TUPAB292 [1.140 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB292

About •

paper received ※ 19 May 2021 paper accepted ※ 02 June 2021 issue date ※ 29 August 2021

Export •

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

TUPAB295

Upgrade to the EPICS Control System at the Argonne Wakefield Accelerator Test Facility

2173

W. Liu, J.M. Byrd, D.S. Doran, G. Ha, A.N. Johnson, P. Piot, J.G. Power, J.H. Shao, G. Shen, C. Whiteford, E.E. Wisniewski
ANL, Lemont, Illinois, USA

Funding: US Department of Energy, Office of Science
The Argonne Wakefield Accelerator (AWA) Test Facility has used a completely homebrewed, MS Windows-based control system for the last 20 years. In an effort to modernize the control system and prepare for an active machine learning program, the AWA will work with the Advanced Photon Source (APS) controls group to upgrade its control system to EPICS. The EPICS control system is expected to facilitate collaborations and support the future growth of AWA. An overview of the previous AWA control and data acquisition system is presented, along with a vision and path for completing the EPICS upgrade.

Poster TUPAB295 [1.108 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB295

About •

paper received ※ 19 May 2021 paper accepted ※ 01 July 2021 issue date ※ 30 August 2021

Export •

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

TUPAB297

Data Archive System for Superconducting RIKEN Linear Accelerator at RIBF

2178

A. Uchiyama, N. Fukunishi, M. Kidera, M. Komiyama
RIKEN Nishina Center, Wako, Japan

At RIKEN Nishina Center, superconducting RIKEN Linear Accelerator (SRILAC) was newly installed at downstream of existing accelerator and upgraded for the search experiments of super-heavy-elements with atomic numbers of 119 and higher. For the data archiving and the data visualization in RI Beam Factory (RIBF) project, we have utilized RIBFCAS (RIBF control archive system) since 2009. For the number of archived data point was expected to increase dramatically for SRILAC, we introduced the Archiver Appliance for improvement of the data archiving performance. On the other hand, to realize a user-friendly system about the data visualization, the data of RIBFCAS and the Archiver Appliance should be visualized on the same system. In this system, by implementing a Web application to convert the RIBFCAS data to JSON format, it became possible to unify the data format with the Archiver Appliance and display the data with the same viewer software. In the SRILAC beam commissioning, it became to useful system for finding anomalies and understanding the behavior of superconducting cavity. In this conference, we report the system implementation, developed tool, and the future plan in detail.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB297

About •

paper received ※ 19 May 2021 paper accepted ※ 10 June 2021 issue date ※ 17 August 2021

Export •

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

TUPAB298

First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT

2182

A. Eichler, F. Burkart, J. Kaiser, W. Kuropka, O. Stein
DESY, Hamburg, Germany
E. Bründermann, A. Santamaria Garcia, C. Xu
KIT, Karlsruhe, Germany

Funding: Helmholtz Artificial Cooperation Unit
Reinforcement Learning algorithms have risen in popularity in recent years in the accelerator physics community, showing potential in beam control and in the optimization and automation of tasks in accelerator operation. The Helmholtz AI project "Machine Learning toward Autonomous Accelerators" is a collaboration between DESY and KIT that works on investigating and developing RL applications for the automatic start-up of electron linear accelerators. The work is carried out in parallel at two similar research accelerators: ARES at DESY and FLUTE at KIT, giving the unique opportunity of transfer learning between facilities. One of the first steps of this project is the establishment of a common interface between the simulations and the machine, in order to test and apply various optimization approaches interchangeably between the two accelerators. In this paper we present the first results on the common interface and its application to beam focusing in ARES, and the idea of laser shaping with spatial light modulators at FLUTE.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB298

About •

paper received ※ 19 May 2021 paper accepted ※ 02 August 2021 issue date ※ 17 August 2021

Export •

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

WEPAB323

High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade

3434

G. Shen, N.D. Arnold, T.G. Berenc, J. Carwardine, E. Chandler, T. Fors, T.J. Madden, D.R. Paskvan, C. Roehrig, S.E. Shoaf, S. Veseli
ANL, Lemont, Illinois, USA

Funding: Argonne National Laboratory’s work was supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under contract DE-AC02-06CH11357.
It is well known that the efficiency of an advanced control algorithm like machine learning is as good as its data quality. Much recent progress in technology enables the massive data acquisition from a control system of modern particle accelerator, and the wide use of embedded controllers, like field-programmable gate arrays (FPGA), provides an opportunity to collect fast data from technical subsystems for monitoring, statistics, diagnostics or fault recording. To improve the data quality, at the APS Upgrade project, a general-purpose data acquisition (DAQ) system is under active development. The APS-U DAQ system collects high-quality fast data from underneath embedded controllers, especially the FPGAs, with the manner of time-correlation and synchronously sampling, which could be used for commissioning, performance monitoring, troubleshooting, and early fault detection, etc. This paper presents the design and latest progress of APS-U high-performance DAQ infrastructure, as well as its preparation to enable the use of machine learning technology for APS-U, and its use cases at APS.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB323

About •

paper received ※ 19 May 2021 paper accepted ※ 24 June 2021 issue date ※ 29 August 2021

Export •

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

THPAB259

High Level Applications for Sirius Accelerators Control

4314

A.C.S. Oliveira, M.B. Alves, L. Liu, X.R. Resende, F.H. de Sá
LNLS, Campinas, Brazil

Sirius is a 4th generation 3 GeV synchrotron light source that has just finalised the first commissioning phase at the Brazilian Center for Research in Energy and Materials (CNPEM) campus in Campinas, Brazil. The large number of process variables and large complexity of the subsystems in this type of machine requires the development of tools to simplify the commissioning and operation of the accelerators. This paper describes some of the high level control tools developed for the accelerators commissioning and future operation.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-THPAB259

About •

paper received ※ 19 May 2021 paper accepted ※ 13 July 2021 issue date ※ 21 August 2021

Export •

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