The Joint Accelerator Conferences Website (JACoW) is an international collaboration that publishes the proceedings of accelerator conferences held around the world.
TY - CONF AU - Debongnie, M. AU - Baylac, M.A. AU - Bouly, F. AU - Chauvin, N. AU - Gatera, A. AU - Junquera, T. AU - Uriot, D. ED - Boland, Mark ED - Tanaka, Hitoshi ED - Button, David ED - Dowd, Rohan ED - Schaa, Volker RW ED - Tan, Eugene TI - Modelization of an Injector With Machine Learning J2 - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019 CY - Melbourne, Australia T2 - International Particle Accelerator Conference T3 - 10 LA - english AB - Modern particle accelerator projects, such as MYRRHA, have very high stability and/or reliability requirements. To meet those, it is necessary to optimize or develop new methods for the control systems. One of the difficulties lies in the relatively long computation time of current beam dynamics codes. In this context, the very low computation time of neural network is of great attraction. However, a neural network has to be trained in order to be of any use. The training of a beam dynamic predictor uses a large dataset (experimental or simulated) that represents the dynamics over the parameter space of interest. Therefore, choosing the right training dataset is crucial for the quality of the neural network predictions. In this work, a study on the sampling choice for the training data is performed to train a neural network to predict the transmission of a beam through a low energy beam transport line and a Radiofrequency Quadrupole. We show and discuss the results obtained on training data set to model the IPHI and MYRRHA injectors. PB - JACoW Publishing CP - Geneva, Switzerland SP - 3096 EP - 3099 KW - network KW - rfq KW - LEBT KW - solenoid KW - proton DA - 2019/06 PY - 2019 SN - 978-3-95450-208-0 DO - DOI: 10.18429/JACoW-IPAC2019-WEPTS006 UR - http://jacow.org/ipac2019/papers/wepts006.pdf ER -