2020/10/25 by Zhanibek Omarov, Omarov, Zhanibek, Selçuk Hacıömeroğlu +1
Engineering · #Accelerator Physics (physics.acc-ph) #Computational Physics (physics.comp-ph) #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Detectors (physics.ins-det) #Non-Destructive Testing Techniques
paper · pdf · doi:10.48550/arxiv.2010.15243
openalex publication_date 2020/10/25 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We present a non-destructive beam profile imaging concept that utilizes machine learning tools, namely genetic algorithm with a gradient descent-like minimization. Electromagnetic fields around a charged beam carry information about its transverse profile. The electrodes of a stripline-type beam position monitor (with eight probes in this study) can pick up that information for visualization of the beam profile. We use a genetic algorithm to transform an arbitrary Gaussian beam in such a way that it eventually reconstructs the transverse position and the shape of the original beam. The algorithm requires a signal that is picked up by the stripline electrodes, and a (precise or approximate) knowledge of the beam size. It can visualize the profile of fairly distorted beams as well.