JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
TY - CONF AU - Tran, A.D. AU - Hao, Y. AU - Martinez Marin, J.L. AU - Mustapha, B. ED - Biedron, Sandra ED - Simakov, Evgenya ED - Milton, Stephen ED - Anisimov, Petr M. ED - Schaa, Volker R.W. TI - Relating Initial Distribution to Beam Loss on the Front End of a Heavy-Ion Linac Using Machine Learning J2 - Proc. of NAPAC2022, Albuquerque, NM, USA, 07-12 August 2022 CY - Albuquerque, NM, USA T2 - International Particle Accelerator Conference T3 - 5 LA - english AB - This work demonstrates using a Neural Network and a Gaussian Process to model the ATLAS front-end. Various neural network architectures were created and trained on the machine settings and outputs to model the phase space projections. The model was then trained on a dataset, with non-linear distortion, to gauge the transferability of the model from simulation to machine. PB - JACoW Publishing CP - Geneva, Switzerland SP - 263 EP - 266 KW - network KW - simulation KW - LEBT KW - emittance KW - controls DA - 2022/10 PY - 2022 SN - 2673-7000 SN - 978-3-95450-232-5 DO - doi:10.18429/JACoW-NAPAC2022-MOPA90 UR - https://jacow.org/napac2022/papers/mopa90.pdf ER -