| Paper |
Title |
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| MOPOPT034 |
Surrogate-Based Bayesian Inference of Transverse Beam Distribution for Non-Stationary Accelerator Systems |
324 |
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- H. Fujii, N. Fukunishi
RIKEN Nishina Center, Wako, Japan
- M. Yamakita
Tokyo Tech, Tokyo, Japan
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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.
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| DOI • |
reference for this paper
※ https://doi.org/10.18429/JACoW-IPAC2022-MOPOPT034
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| About • |
Received ※ 19 May 2022 — Accepted ※ 13 June 2022 — Issue date ※ 17 June 2022 |
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