Sprecher
Beschreibung
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.
