Paper | Title | Other Keywords | Page |
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WEPNEC19 | Optimisation of the PERLE Injector | emittance, gun, electron, cavity | 107 |
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The injector for PERLE, a proposed electron Energy Recovery Linac (ERL) test facility for the LHeC and FCC-eh projects, is intended to deliver 500 pC bunches at a repetition rate of 40.1 MHz for a total beam current of 20 mA. These bunches must have a bunch length of 3 mm rms and an energy of 7 MeV at the entrance to the first linac pass while simultaneously achieving a transverse emittance of less than 6 mm mrad. The injector is based around a DC photocathode electron gun, followed by a focusing and normal conducting bunching section, a booster with 5 independently controllable SRF cavities and a merger into the main ERL. A design for this injector from the photocathode to the exit of the booster is presented. This design was simulated using ASTRA for the beam dynamics simulations and optimized using the many objective optimization algorithm NSGAIII. The use of NSGAIII allows more than three beam parameters to be optimised simultaneously and the trade-offs between them to be explored. | |||
DOI • | reference for this paper ※ https://doi.org/10.18429/JACoW-ERL2019-WEPNEC19 | ||
About • | paper received ※ 01 October 2019 paper accepted ※ 11 November 2019 issue date ※ 24 June 2020 | ||
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WECOYBS04 | Commissioning of theBERLinPro Diagnostics Line using Machine Learning Techniques | gun, MMI, diagnostics, laser | 123 |
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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’ |
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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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