Author: 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.  
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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  
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