Keyword: diagnostics
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WECOYBS04 Commissioning of theBERLinPro Diagnostics Line using Machine Learning Techniques gun, booster, MMI, laser 123
 
  • B.C. Kuske
    HZB, Berlin, Germany
  • A. Adelmann
    PSI, Villigen PSI, Switzerland
 
  Funding: Work supported by German Bundesministerium für Bildung und Forschung, Land Berlin and grants of Helmholtz Association
BERLinPro is an Energy Recovery Linac (ERL) project currently being set up at HZB, Berlin. Commissioning is planned for early 2020. HZB triggered and supported the development of release 2.0 of the particle tracking code OPAL, that is now also applicable to ERLs. OPAL is set up as an open source, highly parallel tracking code for large accelerator systems and many particles. Thus, it is idially suited to serve attempts of applying machine learning approaches to beam dynamics, as demonstrated in [1]. OPAL is used to calculate hundreds of randomized machines close to the commissioning optics of BERLinPro. This data base will be used to train a neural network, to establish a surrogate model of BERLinPro, much faster than any physical model including particle tracking. First steps, like the setup of the sampler and a sensitivity analysis of the resulting data are presented. The ultimate goal of this work is to use machine learning techniques during the commissioning of BERLinPro. Future steps are outlined. [1] A. Edelen, A. Adelmann, N. Neveu, Y. Huber, M. Frey, ’Machine Learning to enable orders of magnitude speedup in multi-objective optimization of particle accelerator systems’
 
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ERL2019-WECOYBS04  
About • paper received ※ 30 October 2019       paper accepted ※ 07 November 2019       issue date ※ 24 June 2020  
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FRCOWBS04 Essential Instrumentation for the Characterization of ERL Beams cavity, radiation, linac, operation 150
 
  • N. Banerjee, A.C. Bartnik, K.E. Deitrick, J. Dobbins, C.M. Gulliford, G.H. Hoffstaetter
    Cornell University (CLASSE), Cornell Laboratory for Accelerator-Based Sciences and Education, Ithaca, New York, USA
  • J.S. Berg, S.J. Brooks, R.J. Michnoff
    BNL, Upton, New York, USA
 
  Funding: This work was performed through the support of New York State Energy Research and Development Agency (NYSERDA).
The typical requirement of Energy Recovery Linacs to produce beams with high repetition rate and high bunch charge presents unique demands on beam diagnostics. ERLs being quite sensitive to time of flight effects necessitate the use of beam arrival time monitors along with typical position detection. Being subjected to a plethora of dynamic effects, both longitudinal and transverse phase space monitoring of the beam becomes quite important. Additionally, beam halo plays an important role determining the overall transmission. Consequently, we also need to characterize halo both directly using sophisticated beam viewers and indirectly using radiation monitors. In this talk, I will describe the instrumentation essential to ERL operation using the Cornell-BNL ERL Test Accelerator (CBETA) as a pertinent example.
 
slides icon Slides FRCOWBS04 [7.129 MB]  
DOI • reference for this paper ※ https://doi.org/10.18429/JACoW-ERL2019-FRCOWBS04  
About • paper received ※ 19 September 2019       paper accepted ※ 01 November 2019       issue date ※ 24 June 2020  
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