IPAC2021 - List of Authors (Fors, T.)

Paper	Title	Page
MOPAB046	Plan for Operating the APS-Upgrade Booster with a Frequency Sweep	201
	J.R. Calvey, T.G. Berenc, A.R. Brill, L. Emery, T. Fors, K.C. Harkay, T.J. Madden, N. Sereno, U. Wienands ANL, Lemont, Illinois, USA A. Gu UCB, Berkeley, California, USA
	The APS-Upgrade presents several challenging demands to the booster synchrotron. Swap-out injection requires the booster to capture a high charge bunch (up to 17 nC), accelerate it to 6 GeV, and maintain a low emittance at extraction for injection into the storage ring. To accommodate these conflicting demands, the RF frequency will be ramped between injection and extraction. However, the RF cavity tuners will remain static, which means the couplers will need to withstand a high reflected power at extraction. This paper presents a plan for a system that will meet the requirements for injection efficiency, extracted emittance, and equivalent power at the coupler. Results from tracking simulations and beam studies with a frequency ramp will also be shown.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB046
About •	paper received ※ 28 May 2021 paper accepted ※ 02 June 2021 issue date ※ 26 August 2021
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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)

Paper

Title

Page

Plan for Operating the APS-Upgrade Booster with a Frequency Sweep

201

J.R. Calvey, T.G. Berenc, A.R. Brill, L. Emery, T. Fors, K.C. Harkay, T.J. Madden, N. Sereno, U. Wienands
ANL, Lemont, Illinois, USA
A. Gu
UCB, Berkeley, California, USA

The APS-Upgrade presents several challenging demands to the booster synchrotron. Swap-out injection requires the booster to capture a high charge bunch (up to 17 nC), accelerate it to 6 GeV, and maintain a low emittance at extraction for injection into the storage ring. To accommodate these conflicting demands, the RF frequency will be ramped between injection and extraction. However, the RF cavity tuners will remain static, which means the couplers will need to withstand a high reflected power at extraction. This paper presents a plan for a system that will meet the requirements for injection efficiency, extracted emittance, and equivalent power at the coupler. Results from tracking simulations and beam studies with a frequency ramp will also be shown.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB046

About •

paper received ※ 28 May 2021 paper accepted ※ 02 June 2021 issue date ※ 26 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)