Machine learning assisted non-destructive transverse beam profile imaging

TL;DR

This paper introduces a non-destructive, machine learning-based method for imaging the transverse profile of charged particle beams using electromagnetic signals and genetic algorithms, enabling visualization of distorted beams.

Contribution

It presents a novel approach combining genetic algorithms and electromagnetic measurements for non-invasive beam profile reconstruction.

Findings

Effective reconstruction of beam profiles from electromagnetic signals.

Capability to visualize distorted beam shapes.

Method applicable with approximate beam size knowledge.

Abstract

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.