IPAC2021 - List of Keywords (data-acquisition)

Paper	Title	Other Keywords	Page
TUPAB291	Subsystem Level Data Acquisition for the Optical Synchronization System at European XFEL	FEL, controls, laser, database	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)

TUPAB295	Upgrade to the EPICS Control System at the Argonne Wakefield Accelerator Test Facility	controls, EPICS, interface, LLRF	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)

WEPAB323	High Performance DAQ Infrastructure to Enable Machine Learning for the Advanced Photon Source Upgrade	monitoring, controls, EPICS, hardware	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)

Paper

Title

Other Keywords

Page

TUPAB291

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

FEL, controls, laser, database

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)

TUPAB295

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

controls, EPICS, interface, LLRF

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)

WEPAB323

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

monitoring, controls, EPICS, hardware

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)