IPAC2021 - List of Authors (Dunning, D.J.)

Paper	Title	Page
TUPAB114	FEL Performance and Beam Quality Assessment of Undulator Line for the CompactLight Facility.	1655
	H.M. Castañeda Cortés, D.J. Dunning, N. Thompson STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
	Funding: H2020 CompactLight has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 777431 The H₂020 CompactLight Project aims for the design of innovative, cost-effective, compact FEL facilities to generate higher peak brilliance radiation in the soft and hard X-ray. In this paper we assess via simulation studies the performance of a variably polarising APPLE-X afterburner positioned downstream of a helical Super Conducting Undulator (SCU). We discuss the optimum balance between the active SCU length and the afterburner length, considering the peak brilliance and pulse energy of the output. Our studies are complemented with analysis of the optical beam quality of the afterburner output to determine the design constraints of the photon beamline that delivers the FEL output to the experimental areas. * Mak, A., Salen, P., Goryashko, V., Clarke, J., http://uu.diva-portal.org/smash/record.jsf?pid=diva2\%3A1280300&dswid=3236 ** Lutman, A. et al. Nature Photonics 10, 468
	Poster TUPAB114 [1.210 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB114
About •	paper received ※ 11 May 2021 paper accepted ※ 10 June 2021 issue date ※ 27 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

WEPAB318	Prediction and Clustering of Longitudinal Phase Space Images and Machine Parameters Using Neural Networks and K-Means Algorithm	3417
	M. Maheshwari STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom D.J. Dunning, J.K. Jones, M.P. King, H.R. Kockelbergh, A.E. Pollard STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
	Machine learning algorithms were used for image and parameter recognition and generation with the aim to optimise the CLARA facility at Daresbury, using start-to-end simulation data. Convolutional and fully connected neural networks were trained using TensorFlow-Keras for different instances, with examples including predicting Longitudinal Phase Space (LPS) images with machine parameters as input and FEL parameter prediction (e.g. pulse energy) from LPS images. The K-means clustering algorithm was used to cluster the LPS images to highlight patterns within the data. Machine learning techniques can enhance the way large amounts of data are processed and analysed and so have great potential for application in accelerator science R&D.
	Poster WEPAB318 [1.062 MB]
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB318
About •	paper received ※ 17 May 2021 paper accepted ※ 05 July 2021 issue date ※ 21 August 2021
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

FEL Performance and Beam Quality Assessment of Undulator Line for the CompactLight Facility.

1655

H.M. Castañeda Cortés, D.J. Dunning, N. Thompson
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom

Funding: H2020 CompactLight has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 777431
The H₂020 CompactLight Project aims for the design of innovative, cost-effective, compact FEL facilities to generate higher peak brilliance radiation in the soft and hard X-ray. In this paper we assess via simulation studies the performance of a variably polarising APPLE-X afterburner positioned downstream of a helical Super Conducting Undulator (SCU). We discuss the optimum balance between the active SCU length and the afterburner length, considering the peak brilliance and pulse energy of the output. Our studies are complemented with analysis of the optical beam quality of the afterburner output to determine the design constraints of the photon beamline that delivers the FEL output to the experimental areas.
* Mak, A., Salen, P., Goryashko, V., Clarke, J., http://uu.diva-portal.org/smash/record.jsf?pid=diva2\%3A1280300&dswid=3236
** Lutman, A. et al. Nature Photonics 10, 468

Poster TUPAB114 [1.210 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-TUPAB114

About •

paper received ※ 11 May 2021 paper accepted ※ 10 June 2021 issue date ※ 27 August 2021

Export •

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

WEPAB318

Prediction and Clustering of Longitudinal Phase Space Images and Machine Parameters Using Neural Networks and K-Means Algorithm

3417

M. Maheshwari
STFC/DL, Daresbury, Warrington, Cheshire, United Kingdom
D.J. Dunning, J.K. Jones, M.P. King, H.R. Kockelbergh, A.E. Pollard
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom

Machine learning algorithms were used for image and parameter recognition and generation with the aim to optimise the CLARA facility at Daresbury, using start-to-end simulation data. Convolutional and fully connected neural networks were trained using TensorFlow-Keras for different instances, with examples including predicting Longitudinal Phase Space (LPS) images with machine parameters as input and FEL parameter prediction (e.g. pulse energy) from LPS images. The K-means clustering algorithm was used to cluster the LPS images to highlight patterns within the data. Machine learning techniques can enhance the way large amounts of data are processed and analysed and so have great potential for application in accelerator science R&D.

Poster WEPAB318 [1.062 MB]

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-WEPAB318

About •

paper received ※ 17 May 2021 paper accepted ※ 05 July 2021 issue date ※ 21 August 2021

Export •

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