Machine learning assisted non-destructive beam profile monitoring

Abstract

We present a non-destructive beam profile monitoring concept that utilizes numerical optimization tools, namely genetic algorithm with a gradient descent-like minimization. The signal picked up by a button BPM includes information about the transverse profile content of the beam. A genetic algorithm is used to transform an arbitrary Gaussian beam in such a way that it eventually reconstructs the transverse position and the shape of the original beam to match the signal on the BPM electrodes. A case study for the developed algorithm is proton EDM experiment where conventional beam profile measurements are not possible. This method allows visualization of fairly distorted beams with non-Gaussian distributions as well.