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 - Fujii, H. AU - Fukunishi, N. AU - Yamakita, M. ED - Zimmermann, Frank ED - Tanaka, Hitoshi ED - Sudmuang, Porntip ED - Klysubun, Prapong ED - Sunwong, Prapaiwan ED - Chanwattana, Thakonwat ED - Petit-Jean-Genaz, Christine ED - Schaa, Volker R.W. TI - Surrogate-Based Bayesian Inference of Transverse Beam Distribution for Non-Stationary Accelerator Systems J2 - Proc. of IPAC2022, Bangkok, Thailand, 12-17 June 2022 CY - Bangkok, Thailand T2 - International Particle Accelerator Conference T3 - 13 LA - english AB - Constraints on the beam diagnostics available in real-time and time-varying beam source conditions make it difficult to provide users with high-quality beams for long periods without interrupting experiments. Although surrogate model-based inference is useful for inferring the unmeasurable, the system states can be incorrectly inferred due to manufacturing errors and neglected higher-order effects when creating the surrogate model. In this paper, we propose to adaptively assimilate the surrogate model for reconstructing the transverse beam distribution with uncertainty and underspecification using a sequential Monte Carlo from the measurements of quadrant beam loss monitors. The proposed method enables sample-efficient and training-free inference and control of the time-varying transverse beam distribution. PB - JACoW Publishing CP - Geneva, Switzerland SP - 324 EP - 327 KW - controls KW - experiment KW - beam-transport KW - framework KW - simulation DA - 2022/07 PY - 2022 SN - 2673-5490 SN - 978-3-95450-227-1 DO - doi:10.18429/JACoW-IPAC2022-MOPOPT034 UR - https://jacow.org/ipac2022/papers/mopopt034.pdf ER -