ICALEPCS2021 - List of Keywords (gun)

Paper	Title	Other Keywords	Page
MOPV018	Linac-200 Gun Control System: Status and Plans	controls, electron, linac, electronics	161
	M.A. Nozdrin, V.V. Kobets, V.F. Minashkin, A. Trifonov JINR, Dubna, Moscow Region, Russia
	Due to the development of the global Tango-based control system for Linac-200 accelerator, the new electron gun control system software was developed. Major gun electronics modification is foreseen. Current gun control system status and modification plans are reported.
	Poster MOPV018 [1.308 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV018
About •	Received ※ 09 October 2021 Revised ※ 19 October 2021 Accepted ※ 04 November 2021 Issue date ※ 03 March 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPV019	Control System for 30 keV Electron Gun Test Facility	controls, electron, PLC, experiment	433
	D.A. Nawaz, M. Ajmal, A. Majid, N.U. Saqib, F. Sher PINSTECH, Islamabad, Pakistan
	At LINAC Project PINSTECH, an electron gun test facility for indigenously developed 30 keV electron guns is developed to control and monitor various beam parameters by performing electron beam tests and diagnostics. After successful testing, electron gun is then integrated into 6 MeV standing wave linear accelerator. This paper presents the control system design and development for the facility.
	Poster TUPV019 [1.468 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV019
About •	Received ※ 10 October 2021 Accepted ※ 20 November 2021 Issue date ※ 09 December 2021
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPV020	Automatic RF and Electron Gun Filament Conditioning Systems for 6 MeV LINAC	vacuum, cavity, electron, controls	437
	A. Majid, D.A. Nawaz, N.U. Saqib, F. Sher PINSTECH, Islamabad, Pakistan
	RF conditioning of vacuum windows and RF cavities is a necessary task for eliminating poor vacuum caused by outgassing and contamination. Also, startup and shutdown process of linear accelerator requires gradual increase and decrease of electron gun filament voltage to avoid damage to the filament. This paper presents an EPICS based multi-loop automatic RF conditioning system and Electron Gun filament conditioning system for Klystron based 6 MeV Linear Accelerator.
	Poster TUPV020 [1.822 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV020
About •	Received ※ 10 October 2021 Revised ※ 17 October 2021 Accepted ※ 20 November 2021 Issue date ※ 26 December 2021
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPV021	Machine Learning for RF Breakdown Detection at CLARA	cavity, network, detector, operation	681
	A.E. Pollard, D.J. Dunning, A.J. Gilfellon STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
	Maximising the accelerating gradient of RF structures is fundamental to improving accelerator facility performance and cost-effectiveness. Structures must be subjected to a conditioning process before operational use, in which the gradient is gradually increased up to the operating value. A limiting effect during this process is breakdown or vacuum arcing, which can cause damage that limits the ultimate operating gradient. Techniques to efficiently condition the cavities while minimising the number of breakdowns are therefore important. In this paper, machine learning techniques are applied to detect breakdown events in RF pulse traces by approaching the problem as anomaly detection, using a variational autoencoder. This process detects deviations from normal operation and classifies them with near perfect accuracy. Offline data from various sources has been used to develop the techniques, which we aim to test at the CLARA facility at Daresbury Laboratory. These techniques could then be applied generally.
	Poster WEPV021 [1.565 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV021
About •	Received ※ 09 October 2021 Accepted ※ 21 November 2021 Issue date ※ 24 November 2021
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THAL03	Machine Learning Based Middle-Layer for Autonomous Accelerator Operation and Control	controls, linac, operation, vacuum	797
	S. Pioli, B. Buonomo, D. Di Giovenale, C. Di Giulio, L.G. Foggetta, G. Piermarini LNF-INFN, Frascati, Italy F. Cardelli, P. Ciuffetti INFN/LNF, Frascati, Italy V. Martinelli INFN/LNL, Legnaro (PD), Italy
	The Singularity project, led by National Laboratories of Frascati of the National Institute for Nuclear Physics (INFN-LNF), aim to develop automated machine-independent middle-layer to control accelerator operation through machine learning (ML) algorithms like Reinforcement Learning (RL) and Cluster integrated with accelerator’s sub-systems. In this work we will present architecture and of the middle-layer made with main purpose to drive user requests through the control framework backend and allow users to enjoy a better User Experience (UX) handling system performances without facing problems due to the interaction with control system. We will report the strategy to develop autonomous operation control with RL algorithms together with the fault detection capability improved by Clustering approach as breakdown and waveguide and RF cavity thermal stability monitor. Results of the first period of operation of this system, currently operating at the electron-positron LINAC of the Dafne complex in Frascati, autonomously controlling accelerator performance in terms of beam transport, beam current optimization and RF cavity phase-jitter compensation will be reported.
	Slides THAL03 [0.960 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL03
About •	Received ※ 19 October 2021 Accepted ※ 22 December 2021 Issue date ※ 16 February 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Other Keywords

Page

MOPV018

Linac-200 Gun Control System: Status and Plans

controls, electron, linac, electronics

161

M.A. Nozdrin, V.V. Kobets, V.F. Minashkin, A. Trifonov
JINR, Dubna, Moscow Region, Russia

Due to the development of the global Tango-based control system for Linac-200 accelerator, the new electron gun control system software was developed. Major gun electronics modification is foreseen. Current gun control system status and modification plans are reported.

Poster MOPV018 [1.308 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-MOPV018

About •

Received ※ 09 October 2021 Revised ※ 19 October 2021 Accepted ※ 04 November 2021 Issue date ※ 03 March 2022

Cite •

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

TUPV019

Control System for 30 keV Electron Gun Test Facility

controls, electron, PLC, experiment

433

D.A. Nawaz, M. Ajmal, A. Majid, N.U. Saqib, F. Sher
PINSTECH, Islamabad, Pakistan

At LINAC Project PINSTECH, an electron gun test facility for indigenously developed 30 keV electron guns is developed to control and monitor various beam parameters by performing electron beam tests and diagnostics. After successful testing, electron gun is then integrated into 6 MeV standing wave linear accelerator. This paper presents the control system design and development for the facility.

Poster TUPV019 [1.468 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV019

About •

Received ※ 10 October 2021 Accepted ※ 20 November 2021 Issue date ※ 09 December 2021

Cite •

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

TUPV020

Automatic RF and Electron Gun Filament Conditioning Systems for 6 MeV LINAC

vacuum, cavity, electron, controls

437

A. Majid, D.A. Nawaz, N.U. Saqib, F. Sher
PINSTECH, Islamabad, Pakistan

RF conditioning of vacuum windows and RF cavities is a necessary task for eliminating poor vacuum caused by outgassing and contamination. Also, startup and shutdown process of linear accelerator requires gradual increase and decrease of electron gun filament voltage to avoid damage to the filament. This paper presents an EPICS based multi-loop automatic RF conditioning system and Electron Gun filament conditioning system for Klystron based 6 MeV Linear Accelerator.

Poster TUPV020 [1.822 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-TUPV020

About •

Received ※ 10 October 2021 Revised ※ 17 October 2021 Accepted ※ 20 November 2021 Issue date ※ 26 December 2021

Cite •

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

WEPV021

Machine Learning for RF Breakdown Detection at CLARA

cavity, network, detector, operation

681

A.E. Pollard, D.J. Dunning, A.J. Gilfellon
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom

Maximising the accelerating gradient of RF structures is fundamental to improving accelerator facility performance and cost-effectiveness. Structures must be subjected to a conditioning process before operational use, in which the gradient is gradually increased up to the operating value. A limiting effect during this process is breakdown or vacuum arcing, which can cause damage that limits the ultimate operating gradient. Techniques to efficiently condition the cavities while minimising the number of breakdowns are therefore important. In this paper, machine learning techniques are applied to detect breakdown events in RF pulse traces by approaching the problem as anomaly detection, using a variational autoencoder. This process detects deviations from normal operation and classifies them with near perfect accuracy. Offline data from various sources has been used to develop the techniques, which we aim to test at the CLARA facility at Daresbury Laboratory. These techniques could then be applied generally.

Poster WEPV021 [1.565 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-WEPV021

About •

Received ※ 09 October 2021 Accepted ※ 21 November 2021 Issue date ※ 24 November 2021

Cite •

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

THAL03

Machine Learning Based Middle-Layer for Autonomous Accelerator Operation and Control

controls, linac, operation, vacuum

797

S. Pioli, B. Buonomo, D. Di Giovenale, C. Di Giulio, L.G. Foggetta, G. Piermarini
LNF-INFN, Frascati, Italy
F. Cardelli, P. Ciuffetti
INFN/LNF, Frascati, Italy
V. Martinelli
INFN/LNL, Legnaro (PD), Italy

The Singularity project, led by National Laboratories of Frascati of the National Institute for Nuclear Physics (INFN-LNF), aim to develop automated machine-independent middle-layer to control accelerator operation through machine learning (ML) algorithms like Reinforcement Learning (RL) and Cluster integrated with accelerator’s sub-systems. In this work we will present architecture and of the middle-layer made with main purpose to drive user requests through the control framework backend and allow users to enjoy a better User Experience (UX) handling system performances without facing problems due to the interaction with control system. We will report the strategy to develop autonomous operation control with RL algorithms together with the fault detection capability improved by Clustering approach as breakdown and waveguide and RF cavity thermal stability monitor. Results of the first period of operation of this system, currently operating at the electron-positron LINAC of the Dafne complex in Frascati, autonomously controlling accelerator performance in terms of beam transport, beam current optimization and RF cavity phase-jitter compensation will be reported.

Slides THAL03 [0.960 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-ICALEPCS2021-THAL03

About •

Received ※ 19 October 2021 Accepted ※ 22 December 2021 Issue date ※ 16 February 2022

Cite •

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