Simulation-Based Inference for Beam Parameter Inversion
Simulation-Based Inference for Beam Parameter Inversion
In this talk, I'd like to present modern machine learning tools for estimating the posterior of the inverse problem exposed in a beam control setting. That is, given an experimental beam profile, I'd like to demonstrate tools that help to estimate which simulation parameters might have produced a similar beam profile with high likelihood.
We summarize preliminary findings bound to optimize a xray beamline located at a synchrotron accelerator. With this, we hope to tackle the challenge to characterize beam quality with minimal invasion as possible. The basis of my discussion will be a surrogate model that emulates experimental conditions of beam profile knife-edge scans. We hope that this discussion is of interest to this accelerator physics community at LPA.
Keywords: laser-plasma acceleration; simulation based inference; machine learning; modelling
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Poster
(Online presentation)
LPA Online Workshop on Control Systems and Machine Learning 24-27 January 2022, 24.-28.01.2022, online, www
DOI: 10.6084/m9.figshare.19071641.v1
Permalink: https://www.hzdr.de/publications/Publ-34139