IPAC2021 - List of Authors (Jones, J.K.)

Paper	Title	Page
MOPAB071	Progress with the Booster Design for the Diamond-II Upgrade	286
	I.P.S. Martin, C. Christou, M.P. Cox, R.T. Fielder, J. Kallestrup, A. Shahveh, W. Tizzano DLS, Oxfordshire, United Kingdom A.D. Brynes, J.K. Jones, B.D. Muratori, H.L. Owen STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom
	Efficient injection into the Diamond-II storage ring [, ] will require an emittance and bunch length substantially below the values produced from the existing booster. Whilst an earlier design for a replacement based on TME cells was able to meet the target values of <30 nm.rad and <40 ps respectively [, *], several technical constraints have led to a rethink of this solution. The revised booster lattice utilises a larger number of cells based on combined-function magnets with lower peak fields that still meets the emittance and bunch length goals. In addition, the new ring has been designed to have low impedance to maximise the extracted charge per shot. In this paper we describe the main features of the lattice, present the status of the engineering design and quantify the expected performance. Diamond-II Conceptual Design Report, Diamond Light Source H. Ghasem et al, these proceedings I. Martin, R. Bartolini, J.Phys.:Conf. Ser., 1067, 032005 ***I. Martin et al, IPAC 2019, WEPMP042
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB071
About •	paper received ※ 18 May 2021 paper accepted ※ 31 May 2021 issue date ※ 02 September 2021
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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

Progress with the Booster Design for the Diamond-II Upgrade

286

I.P.S. Martin, C. Christou, M.P. Cox, R.T. Fielder, J. Kallestrup, A. Shahveh, W. Tizzano
DLS, Oxfordshire, United Kingdom
A.D. Brynes, J.K. Jones, B.D. Muratori, H.L. Owen
STFC/DL/ASTeC, Daresbury, Warrington, Cheshire, United Kingdom

Efficient injection into the Diamond-II storage ring [*, **] will require an emittance and bunch length substantially below the values produced from the existing booster. Whilst an earlier design for a replacement based on TME cells was able to meet the target values of <30 nm.rad and <40 ps respectively [***, ****], several technical constraints have led to a rethink of this solution. The revised booster lattice utilises a larger number of cells based on combined-function magnets with lower peak fields that still meets the emittance and bunch length goals. In addition, the new ring has been designed to have low impedance to maximise the extracted charge per shot. In this paper we describe the main features of the lattice, present the status of the engineering design and quantify the expected performance.
*Diamond-II Conceptual Design Report, Diamond Light Source
**H. Ghasem et al, these proceedings
***I. Martin, R. Bartolini, J.Phys.:Conf. Ser., 1067, 032005
****I. Martin et al, IPAC 2019, WEPMP042

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IPAC2021-MOPAB071

About •

paper received ※ 18 May 2021 paper accepted ※ 31 May 2021 issue date ※ 02 September 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)