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TY - CONF AU - Kuske, B.C. AU - Adelmann, A. ED - Meseck, Atoosa ED - McAteer, Meghan ED - Schaa, Volker R.W. ED - V\xF6lker, Jens TI - Commissioning of theBERLinPro Diagnostics Line using Machine Learning Techniques J2 - Proc. of ERL2019, Berlin, Germany, 15-20 September 2019 CY - Berlin, Germany T2 - ICFA Advanced Beam Dynamics Workshop on Energy Recovery Linacs T3 - 63 LA - english AB - 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’ PB - JACoW Publishing CP - Geneva, Switzerland SP - 123 EP - 128 KW - gun KW - booster KW - MMI KW - diagnostics KW - laser DA - 2020/06 PY - 2020 SN - 978-3-95450-217-2 DO - doi:10.18429/JACoW-ERL2019-WECOYBS04 UR - http://jacow.org/erl2019/papers/wecoybs04.pdf ER -