LINAC2022 - List of Authors (Dunning, D.J.)

Paper	Title	Page
THPOJO09	Status of CLARA at Daresbury Laboratory	711
	D. Angal-Kalinin, A.R. Bainbridge, A.D. Brynes, R.K. Buckley, S.R. Buckley, H.M. Castañeda Cortés, J.A. Clarke, L.S. Cowie, K.D. Dumbell, D.J. Dunning, A.J. Gilfellon, A.R. Goulden, J. Henderson, S. Hitchen, F. Jackson, C.R. Jenkins, M.A. Johnson, J.K. Jones, N.Y. Joshi, M.P. King, S.L. Mathisen, J.W. McKenzie, R. Mclean, K.J. Middleman, B.L. Militsyn, K.T. Morrow, A.J. Moss, B.D. Muratori, T.C.Q. Noakes, W.A. Okell, H.L. Owen, T.H. Pacey, A.E. Pollard, M.D. Roper, Y.M. Saveliev, D.J. Scott, B.J.A. Shepherd, R.J. Smith, E.W. Snedden, N. Thompson, C. Tollervey, R. Valizadeh, D.A. Walsh, A.E. Wheelhouse, P.H. Williams STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom A.R. Bainbridge, A.D. Brynes, J.A. Clarke, L.S. Cowie, K.D. Dumbell, D.J. Dunning, C.R. Jenkins, K.J. Middleman, A.J. Moss, B.D. Muratori, H.L. Owen, Y.M. Saveliev, D.J. Scott, B.J.A. Shepherd, N. Thompson, R. Valizadeh, A.J. Vick, A.E. Wheelhouse Cockcroft Institute, Warrington, Cheshire, United Kingdom A.D. Brynes Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy R.J. Cash, R.F. Clarke, M. Colling, G. Cox, B.D. Fell, S.A. Griffiths, M.D. Hancock, T. Hartnett, J.P. Hindley, C. Hodgkinson, G. Marshall, A. Oates, A.J. Vick, J.T.G. Wilson STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom J. Henderson Cockcroft Institute, Lancaster University, Lancaster, United Kingdom
	CLARA (Compact Linear Accelerator for Research and Applications) is a test facility for Free Electron Laser (FEL) research and other applications at STFC’s Daresbury Laboratory. The Front End of CLARA has been used for user exploitation programme from 2018. The second exploitation period in 2021-22 provided a range of beam parameters to 8 user experiments. We report on the status, further machine development, and future plans for CLARA including Full Energy Beam Exploitation (FEBE) beamline which will provide 250 MeV/c high brightness beam for novel experiments.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOJO09
About •	Received ※ 19 August 2022 — Revised ※ 28 August 2022 — Accepted ※ 05 September 2022 — Issue date ※ 15 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

MOPORI05	Application of Virtual Diagnostics in the FEBE Clara User Area	231
	J. Wolfenden, C. Swain, C.P. Welsch The University of Liverpool, Liverpool, United Kingdom D.J. Dunning, J.K. Jones, T.H. Pacey, A.E. Pollard STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom C. Swain, C.P. Welsch, J. Wolfenden Cockcroft Institute, Warrington, Cheshire, United Kingdom
	Funding: This work is supported by the AWAKE-UK phase II project funded by STFC and the STFC Cockcroft core grant No. ST/G008248/1. Successful user experiments at particle beam facilities are dependent upon the awareness of beam characteristics at the interaction point. Often, properties are measured beforehand for fixed operation modes; users then rely on the long-term stability of the beam. Otherwise, diagnostics must be integrated into a user experiment, costing resources and limiting space in the user area. This contribution proposes the application of machine learning to develop a suite of virtual diagnostic systems. Virtual diagnostics take data at easy to access locations, and infer beam properties at locations where a measurement has not been taken, and often cannot be taken. Here the focus is the user area at the planned Full Energy Beam Exploitation (FEBE) upgrade to the CLARA facility (UK). Presented is a simulation-based proof-of-concept for a variety of virtual diagnostics. Transverse and longitudinal properties are measured upstream of the user area, coupled with the beam optics parameters leading to the user area, and input into a neural network, to predict the same parameters within the user area. Potential instrumentation for FEBE CLARA virtual diagnostics will also be discussed.
	Poster MOPORI05 [0.613 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPORI05
About •	Received ※ 17 August 2022 — Revised ※ 22 August 2022 — Accepted ※ 28 August 2022 — Issue date ※ 01 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

TUPORI14	A Start-to-End Optimisation Strategy for the CompactLight Accelerator Beamline	573
	Y. Zhao, A. Latina CERN, Meyrin, Switzerland A. Aksoy Ankara University, Accelerator Technologies Institute, Golbasi, Turkey H.M. Castañeda Cortés, D.J. Dunning, N. Thompson STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
	The CompactLight collaboration designed a compact and cost-effective hard X-ray FEL facility, complemented by a soft X-ray option, based on X-band acceleration, capable of operating at 1 kHz pulse repetition rate. In this paper, we present a new simple start-to-end optimisation strategy that is developed for the CompactLight accelerator beamline, focusing on the hard X-ray mode. The optimisation is divided into two steps. The first step improves the electron beam quality that finally leads to a better FEL performance by optimising the major parameters of the beamline. The second step provides matched twiss parameters for the FEL undulator by tuning the matching quadrupoles at the end of the accelerator beamline. A single objective optimisation method, with different objective functions, is used to optimise the performance. The sensitivity of the results to jitters is also minimised by including their effects in the final objective function.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-TUPORI14
About •	Received ※ 15 August 2022 — Revised ※ 31 August 2022 — Accepted ※ 31 August 2022 — Issue date ※ 15 September 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

THPORI16	Machine Learning for RF Breakdown Detection at CLARA	858
	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. Deployment of the machine learning system on the high repetition rate gun upgrade at CLARA has begun.
	Poster THPORI16 [2.099 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPORI16
About •	Received ※ 22 August 2022 — Revised ※ 30 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 15 October 2022
Cite •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Status of CLARA at Daresbury Laboratory

711

D. Angal-Kalinin, A.R. Bainbridge, A.D. Brynes, R.K. Buckley, S.R. Buckley, H.M. Castañeda Cortés, J.A. Clarke, L.S. Cowie, K.D. Dumbell, D.J. Dunning, A.J. Gilfellon, A.R. Goulden, J. Henderson, S. Hitchen, F. Jackson, C.R. Jenkins, M.A. Johnson, J.K. Jones, N.Y. Joshi, M.P. King, S.L. Mathisen, J.W. McKenzie, R. Mclean, K.J. Middleman, B.L. Militsyn, K.T. Morrow, A.J. Moss, B.D. Muratori, T.C.Q. Noakes, W.A. Okell, H.L. Owen, T.H. Pacey, A.E. Pollard, M.D. Roper, Y.M. Saveliev, D.J. Scott, B.J.A. Shepherd, R.J. Smith, E.W. Snedden, N. Thompson, C. Tollervey, R. Valizadeh, D.A. Walsh, A.E. Wheelhouse, P.H. Williams
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
A.R. Bainbridge, A.D. Brynes, J.A. Clarke, L.S. Cowie, K.D. Dumbell, D.J. Dunning, C.R. Jenkins, K.J. Middleman, A.J. Moss, B.D. Muratori, H.L. Owen, Y.M. Saveliev, D.J. Scott, B.J.A. Shepherd, N. Thompson, R. Valizadeh, A.J. Vick, A.E. Wheelhouse
Cockcroft Institute, Warrington, Cheshire, United Kingdom
A.D. Brynes
Elettra-Sincrotrone Trieste S.C.p.A., Basovizza, Italy
R.J. Cash, R.F. Clarke, M. Colling, G. Cox, B.D. Fell, S.A. Griffiths, M.D. Hancock, T. Hartnett, J.P. Hindley, C. Hodgkinson, G. Marshall, A. Oates, A.J. Vick, J.T.G. Wilson
STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
J. Henderson
Cockcroft Institute, Lancaster University, Lancaster, United Kingdom

CLARA (Compact Linear Accelerator for Research and Applications) is a test facility for Free Electron Laser (FEL) research and other applications at STFC’s Daresbury Laboratory. The Front End of CLARA has been used for user exploitation programme from 2018. The second exploitation period in 2021-22 provided a range of beam parameters to 8 user experiments. We report on the status, further machine development, and future plans for CLARA including Full Energy Beam Exploitation (FEBE) beamline which will provide 250 MeV/c high brightness beam for novel experiments.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPOJO09

About •

Received ※ 19 August 2022 — Revised ※ 28 August 2022 — Accepted ※ 05 September 2022 — Issue date ※ 15 September 2022

Cite •

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

MOPORI05

Application of Virtual Diagnostics in the FEBE Clara User Area

231

J. Wolfenden, C. Swain, C.P. Welsch
The University of Liverpool, Liverpool, United Kingdom
D.J. Dunning, J.K. Jones, T.H. Pacey, A.E. Pollard
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
C. Swain, C.P. Welsch, J. Wolfenden
Cockcroft Institute, Warrington, Cheshire, United Kingdom

Funding: This work is supported by the AWAKE-UK phase II project funded by STFC and the STFC Cockcroft core grant No. ST/G008248/1.
Successful user experiments at particle beam facilities are dependent upon the awareness of beam characteristics at the interaction point. Often, properties are measured beforehand for fixed operation modes; users then rely on the long-term stability of the beam. Otherwise, diagnostics must be integrated into a user experiment, costing resources and limiting space in the user area. This contribution proposes the application of machine learning to develop a suite of virtual diagnostic systems. Virtual diagnostics take data at easy to access locations, and infer beam properties at locations where a measurement has not been taken, and often cannot be taken. Here the focus is the user area at the planned Full Energy Beam Exploitation (FEBE) upgrade to the CLARA facility (UK). Presented is a simulation-based proof-of-concept for a variety of virtual diagnostics. Transverse and longitudinal properties are measured upstream of the user area, coupled with the beam optics parameters leading to the user area, and input into a neural network, to predict the same parameters within the user area. Potential instrumentation for FEBE CLARA virtual diagnostics will also be discussed.

Poster MOPORI05 [0.613 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-MOPORI05

About •

Received ※ 17 August 2022 — Revised ※ 22 August 2022 — Accepted ※ 28 August 2022 — Issue date ※ 01 September 2022

Cite •

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

TUPORI14

A Start-to-End Optimisation Strategy for the CompactLight Accelerator Beamline

573

Y. Zhao, A. Latina
CERN, Meyrin, Switzerland
A. Aksoy
Ankara University, Accelerator Technologies Institute, Golbasi, Turkey
H.M. Castañeda Cortés, D.J. Dunning, N. Thompson
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom

The CompactLight collaboration designed a compact and cost-effective hard X-ray FEL facility, complemented by a soft X-ray option, based on X-band acceleration, capable of operating at 1 kHz pulse repetition rate. In this paper, we present a new simple start-to-end optimisation strategy that is developed for the CompactLight accelerator beamline, focusing on the hard X-ray mode. The optimisation is divided into two steps. The first step improves the electron beam quality that finally leads to a better FEL performance by optimising the major parameters of the beamline. The second step provides matched twiss parameters for the FEL undulator by tuning the matching quadrupoles at the end of the accelerator beamline. A single objective optimisation method, with different objective functions, is used to optimise the performance. The sensitivity of the results to jitters is also minimised by including their effects in the final objective function.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-TUPORI14

About •

Received ※ 15 August 2022 — Revised ※ 31 August 2022 — Accepted ※ 31 August 2022 — Issue date ※ 15 September 2022

Cite •

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

THPORI16

Machine Learning for RF Breakdown Detection at CLARA

858

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. Deployment of the machine learning system on the high repetition rate gun upgrade at CLARA has begun.

Poster THPORI16 [2.099 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-LINAC2022-THPORI16

About •

Received ※ 22 August 2022 — Revised ※ 30 August 2022 — Accepted ※ 01 September 2022 — Issue date ※ 15 October 2022

Cite •

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