IBIC2023 - List of Keywords (real-time)

Paper	Title	Other Keywords	Page
MOP036	A New Approach for Canadian Light Source Future Orbit Correction System Driven by Neural Network	network, ECR, storage-ring, experiment	102
	S. Saadat, M.J. Boland CLS, Saskatoon, Saskatchewan, Canada M.J. Boland University of Saskatchewan, Saskatoon, Canada
	The Orbit Correction System (OCS) of the CLS comprises 48 sets of BPMs. Each BPM has the ability to measure the position of the beam in both the X-Y directions and can record data at a rate of 900 times per second. The Inverse Response Matrix is utilized to determine the optimal strength of the 48 sets of orbit correctors in both the X-Y directions, in order to ensure that the beam follows its desired path. The Singular Value Decomposition function is replaced by a neural network algorithm to serve as the brain of the orbit correction system in this study. The training model’s design includes three hidden layers, and within each layer, there are 96 nodes. The neural network’s outputs for regular operations in CLS exhibit a Mean Square Error of 10^-7. Various difficult scenarios were created to test the OCS at 8.0 mA, using offsets in different sections of the storage ring. However, the new model was able to produce the necessary Orbit Correctors signals without any trouble.
	Poster MOP036 [1.438 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2023-MOP036
About •	Received ※ 14 July 2023 — Revised ※ 09 September 2023 — Accepted ※ 28 September 2023 — Issue date ※ 30 September 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TU3C02	FPGA Architectures for Distributed ML Systems for Real-Time Beam Loss De-Blending	network, distributed, operation, FPGA	160
	M.A. Ibrahim, J.M.S. Arnold, M.R. Austin, J.R. Berlioz, P.M. Hanlet, K.J. Hazelwood, J. Mitrevski, V.P. Nagaslaev, A. Narayanan, D.J. Nicklaus, G. Pradhan, A.L. Saewert, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran Fermilab, Batavia, Illinois, USA J.YC. Hu, J. Jiang, H. Liu, S. Memik, R. Shi, A.M. Shuping, M. Thieme, C. Xu Northwestern University, EVANSTON, USA
	Funding: Operated by Fermi Research Alliance, LLC under Contract No.DE-AC02-07CH11359 with the United States Department of Energy. Additional funding provided by Grant Award No. LAB 20-2261 [1] The Real-time Edge AI for Distributed Systems (READS) project’s goal is to create a Machine Learning (ML) system for real-time beam loss de-blending within the accelerator enclosure, which houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure¿s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. The ML system will automatically disentangle each machine¿s contributions to those measured losses, while not disrupting the existing operations-critical functions of the BLM system. Within this paper, the ML models, used for learning both local and global machine signatures and producing high quality inferences based on raw BLM loss measurements, will only be discussed at a high-level. This paper will focus on the evolution of the architecture, which provided the high-frequency, low-latency collection of synchronized data streams to make real-time inferences. Performed at Northwestern with support from the Departments of Computer Science and Electrical and Computer Engineering
	Slides TU3C02 [17.830 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2023-TU3C02
About •	Received ※ 07 September 2023 — Revised ※ 10 September 2023 — Accepted ※ 12 September 2023 — Issue date ※ 25 September 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP045	Real Time Momentum Spread Measurement of the CERN Antiproton Decelerator Beam	operation, diagnostics, hardware, software	293
	P. Freyermuth, B. Dupuy, D. Gamba CERN, Meyrin, Switzerland
	Constant optimisation and diagnostics of the cooling processes in the CERN antiproton decelerator (AD) relies on a de-bunched beam momentum spread real time measurement. This article will describe the renovation of the acquisition chain of the longitudinal Schottky diagnostics in the AD, using standard CERN hardware and software to maximize reliability, ease maintenance, and meet the requirements for standard operational tools. The whole chain, from the pick-up to the operation software applications will be described with emphasis on the implementation of the data processing running on the front-end computer. Limitations will also be discussed and outlook for further development given.
	Poster TUP045 [21.199 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2023-TUP045
About •	Received ※ 05 September 2023 — Revised ※ 08 September 2023 — Accepted ※ 14 September 2023 — Issue date ※ 27 September 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUP046	Status of the RFSoC-based Signal Processing for Multi-bunch and Filling Pattern Feedbacks in the SLS 2.0	feedback, controls, storage-ring, software	297
	P.H. Baeta Neves Diniz Santos PSI, Villigen PSI, Switzerland
	Having effectively evaluated the RF System-On-Chip (RFSoC) as a suitable technology for the SLS2.0 Filling Pattern Feedback (FPFB) and Multi-bunch Feedback (MBFB) [1], our current focus lies in realizing and expanding the required real-time Digital Signal Processing (DSP) algorithms on an RFSoC evaluation board. This contribution outlines the present status of our feedback systems, including recent outcomes derived from testing prototypes both in the laboratory and with beam signals at the storage ring. [1] P. Baeta et al., "RF System-on-Chip for Multi-Bunch and Filling-Pattern Feedbacks," Proc. IBIC’22
	Poster TUP046 [1.201 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2023-TUP046
About •	Received ※ 30 August 2023 — Revised ※ 09 September 2023 — Accepted ※ 12 September 2023 — Issue date ※ 29 September 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEP011	A Preliminary Design of Bunch-by-bunch 3D Positions Measurement	storage-ring, SRF, data-acquisition, timing	347
	R.Z. Wu, P. Lu, B.G. Sun, L.L. Tang, D.Y. Wang, Y.K. Zhao USTC/NSRL, Hefei, Anhui, People’s Republic of China
	The decrease of beam emittance in the 4th generation light source greatly increases the electron density, thus the wakefields and beam impedance in the storage ring are significantly enhanced, resulting in various beam instabilities. Therefore, it is necessary to observe the transient state of beams using the bunch-by-bunch technique, so as to dig into these instabilities. Here a three-dimensional (3D) positions measurement instrument is designed based on data synchronization module (DSM) to acquire the transverse positions and longitudinal phases of beams in real-time.
	Poster WEP011 [0.657 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2023-WEP011
About •	Received ※ 12 July 2023 — Revised ※ 09 September 2023 — Accepted ※ 13 September 2023 — Issue date ※ 27 September 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEP013	Quality Assurance of Proton Beam Profile Using Phosphor Screen and TE-Cooled CMOS Camera	proton, radiation, monitoring, experiment	350
	G.I. Jung Korea Atomic Energy Research Institute (KAERI), Daejeon, Republic of Korea Y.S. Hwang, Y.J. Yoon KOMAC, KAERI, Gyeongju, Republic of Korea
	Funding: This work has benn supported through KOMAC (Korea of Multi-purpose Accelerator Complex) operation fund of KAERI by MSIT (Ministry of Science and ICT The KOMAC (Korea Multi-purpose Accelerator Complex) has operated 100-MeV proton linear accelerator and provide high flux proton beam at the TR10³, a general purpose irradiation facility. To uniformly irradiate the sample with protons, it is important to confirm the beam profile uniformity through the quality assurance (QA) process. Recently, for real-time and in-situ proton beam profile monitoring at the TR103, P43 phosphor screen and TE-cooled CMOS camera were introduced and tested. The camera captured images of the emitted light as protons with energy of 15, 42, 100 MeV were incident. A software for selecting beam profile image and post-processing of image data such as background subtraction, image smoothing, geometrical correction, selecting Region Of Interest (ROI) and X-Y coordination was developed using Python. Measured beam profiles using phosphor screen and cooled camera were compared to Gafchromic film. The linearity between light output and beam flux were measured. In this study, we will discuss the test results of proton beam profile measurement using phosphor screen and TE-cooled CMOS camera for introduction to quality assurance process at the TR103.
	Poster WEP013 [1.392 MB]
DOI •	reference for this paper ※ doi:10.18429/JACoW-IBIC2023-WEP013
About •	Received ※ 29 August 2023 — Revised ※ 09 September 2023 — Accepted ※ 10 September 2023 — Issue date ※ 10 September 2023
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOP036

A New Approach for Canadian Light Source Future Orbit Correction System Driven by Neural Network

network, ECR, storage-ring, experiment

102

S. Saadat, M.J. Boland
CLS, Saskatoon, Saskatchewan, Canada
M.J. Boland
University of Saskatchewan, Saskatoon, Canada

The Orbit Correction System (OCS) of the CLS comprises 48 sets of BPMs. Each BPM has the ability to measure the position of the beam in both the X-Y directions and can record data at a rate of 900 times per second. The Inverse Response Matrix is utilized to determine the optimal strength of the 48 sets of orbit correctors in both the X-Y directions, in order to ensure that the beam follows its desired path. The Singular Value Decomposition function is replaced by a neural network algorithm to serve as the brain of the orbit correction system in this study. The training model’s design includes three hidden layers, and within each layer, there are 96 nodes. The neural network’s outputs for regular operations in CLS exhibit a Mean Square Error of 10^-7. Various difficult scenarios were created to test the OCS at 8.0 mA, using offsets in different sections of the storage ring. However, the new model was able to produce the necessary Orbit Correctors signals without any trouble.

Poster MOP036 [1.438 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2023-MOP036

About •

Received ※ 14 July 2023 — Revised ※ 09 September 2023 — Accepted ※ 28 September 2023 — Issue date ※ 30 September 2023

Cite •

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

TU3C02

FPGA Architectures for Distributed ML Systems for Real-Time Beam Loss De-Blending

network, distributed, operation, FPGA

160

M.A. Ibrahim, J.M.S. Arnold, M.R. Austin, J.R. Berlioz, P.M. Hanlet, K.J. Hazelwood, J. Mitrevski, V.P. Nagaslaev, A. Narayanan, D.J. Nicklaus, G. Pradhan, A.L. Saewert, B.A. Schupbach, K. Seiya, R.M. Thurman-Keup, N.V. Tran
Fermilab, Batavia, Illinois, USA
J.YC. Hu, J. Jiang, H. Liu, S. Memik, R. Shi, A.M. Shuping, M. Thieme, C. Xu
Northwestern University, EVANSTON, USA

Funding: Operated by Fermi Research Alliance, LLC under Contract No.DE-AC02-07CH11359 with the United States Department of Energy. Additional funding provided by Grant Award No. LAB 20-2261 [1]
The Real-time Edge AI for Distributed Systems (READS) project’s goal is to create a Machine Learning (ML) system for real-time beam loss de-blending within the accelerator enclosure, which houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure¿s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. The ML system will automatically disentangle each machine¿s contributions to those measured losses, while not disrupting the existing operations-critical functions of the BLM system. Within this paper, the ML models, used for learning both local and global machine signatures and producing high quality inferences based on raw BLM loss measurements, will only be discussed at a high-level. This paper will focus on the evolution of the architecture, which provided the high-frequency, low-latency collection of synchronized data streams to make real-time inferences.
Performed at Northwestern with support from the Departments of Computer Science and Electrical and Computer Engineering

Slides TU3C02 [17.830 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2023-TU3C02

About •

Received ※ 07 September 2023 — Revised ※ 10 September 2023 — Accepted ※ 12 September 2023 — Issue date ※ 25 September 2023

Cite •

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

TUP045

Real Time Momentum Spread Measurement of the CERN Antiproton Decelerator Beam

operation, diagnostics, hardware, software

293

P. Freyermuth, B. Dupuy, D. Gamba
CERN, Meyrin, Switzerland

Constant optimisation and diagnostics of the cooling processes in the CERN antiproton decelerator (AD) relies on a de-bunched beam momentum spread real time measurement. This article will describe the renovation of the acquisition chain of the longitudinal Schottky diagnostics in the AD, using standard CERN hardware and software to maximize reliability, ease maintenance, and meet the requirements for standard operational tools. The whole chain, from the pick-up to the operation software applications will be described with emphasis on the implementation of the data processing running on the front-end computer. Limitations will also be discussed and outlook for further development given.

Poster TUP045 [21.199 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2023-TUP045

About •

Received ※ 05 September 2023 — Revised ※ 08 September 2023 — Accepted ※ 14 September 2023 — Issue date ※ 27 September 2023

Cite •

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

TUP046

Status of the RFSoC-based Signal Processing for Multi-bunch and Filling Pattern Feedbacks in the SLS 2.0

feedback, controls, storage-ring, software

297

P.H. Baeta Neves Diniz Santos
PSI, Villigen PSI, Switzerland

Having effectively evaluated the RF System-On-Chip (RFSoC) as a suitable technology for the SLS2.0 Filling Pattern Feedback (FPFB) and Multi-bunch Feedback (MBFB) [1], our current focus lies in realizing and expanding the required real-time Digital Signal Processing (DSP) algorithms on an RFSoC evaluation board. This contribution outlines the present status of our feedback systems, including recent outcomes derived from testing prototypes both in the laboratory and with beam signals at the storage ring.
[1] P. Baeta et al., "RF System-on-Chip for Multi-Bunch and Filling-Pattern Feedbacks," Proc. IBIC’22

Poster TUP046 [1.201 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2023-TUP046

About •

Received ※ 30 August 2023 — Revised ※ 09 September 2023 — Accepted ※ 12 September 2023 — Issue date ※ 29 September 2023

Cite •

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

WEP011

A Preliminary Design of Bunch-by-bunch 3D Positions Measurement

storage-ring, SRF, data-acquisition, timing

347

R.Z. Wu, P. Lu, B.G. Sun, L.L. Tang, D.Y. Wang, Y.K. Zhao
USTC/NSRL, Hefei, Anhui, People’s Republic of China

The decrease of beam emittance in the 4th generation light source greatly increases the electron density, thus the wakefields and beam impedance in the storage ring are significantly enhanced, resulting in various beam instabilities. Therefore, it is necessary to observe the transient state of beams using the bunch-by-bunch technique, so as to dig into these instabilities. Here a three-dimensional (3D) positions measurement instrument is designed based on data synchronization module (DSM) to acquire the transverse positions and longitudinal phases of beams in real-time.

Poster WEP011 [0.657 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2023-WEP011

About •

Received ※ 12 July 2023 — Revised ※ 09 September 2023 — Accepted ※ 13 September 2023 — Issue date ※ 27 September 2023

Cite •

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

WEP013

Quality Assurance of Proton Beam Profile Using Phosphor Screen and TE-Cooled CMOS Camera

proton, radiation, monitoring, experiment

350

G.I. Jung
Korea Atomic Energy Research Institute (KAERI), Daejeon, Republic of Korea
Y.S. Hwang, Y.J. Yoon
KOMAC, KAERI, Gyeongju, Republic of Korea

Funding: This work has benn supported through KOMAC (Korea of Multi-purpose Accelerator Complex) operation fund of KAERI by MSIT (Ministry of Science and ICT
The KOMAC (Korea Multi-purpose Accelerator Complex) has operated 100-MeV proton linear accelerator and provide high flux proton beam at the TR10³, a general purpose irradiation facility. To uniformly irradiate the sample with protons, it is important to confirm the beam profile uniformity through the quality assurance (QA) process. Recently, for real-time and in-situ proton beam profile monitoring at the TR103, P43 phosphor screen and TE-cooled CMOS camera were introduced and tested. The camera captured images of the emitted light as protons with energy of 15, 42, 100 MeV were incident. A software for selecting beam profile image and post-processing of image data such as background subtraction, image smoothing, geometrical correction, selecting Region Of Interest (ROI) and X-Y coordination was developed using Python. Measured beam profiles using phosphor screen and cooled camera were compared to Gafchromic film. The linearity between light output and beam flux were measured. In this study, we will discuss the test results of proton beam profile measurement using phosphor screen and TE-cooled CMOS camera for introduction to quality assurance process at the TR103.

Poster WEP013 [1.392 MB]

DOI •

reference for this paper ※ doi:10.18429/JACoW-IBIC2023-WEP013

About •

Received ※ 29 August 2023 — Revised ※ 09 September 2023 — Accepted ※ 10 September 2023 — Issue date ※ 10 September 2023

Cite •

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