Abstract

Measurements of undistorted transverse profiles via Ionization Profile Monitors (IPMs) may pose a great challenge for high brightness or high energy beams due to interaction of ionized electrons or ions with the electromagnetic field of the beam. This contribution presents application of various machine learning algorithms to the problem of inferring the actual beam profile width from measured profiles that are distorted by beam space-charge interaction. (Generalized) linear regression, artificial neural network and support vector machine algorithms are trained with simulation data, obtained from the Virtual-IPM simulation tool, in order to learn the relation between distorted profiles and original beam dimension. The performance of different algorithms is assessed and the obtained results are very promising with simulation data.

Highlights

  • Ionization Profile Monitors (IPM) are used for nondestructive measurements of the transverse beam profile

  • In IPM, the deformation of measured beam profile occurs as a result of effects related to ionization, transport of electrons to the detector or detector-related effects such as non-uniform response of Multi-Channel Plate (MCP)

  • Corresponds to the following decision function: Support Vector Machine Regression yp = WT x + b where W is an array of coefficients ("weights") and b is a bias term, both being adjusted during the fitting procedure

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Summary

Introduction

Ionization Profile Monitors (IPM) are used for nondestructive measurements of the transverse beam profile. The distribution of those ions and electrons follows the charge distribution of the beam and the IPM measures a corresponding one-dimensional projection. In cases where the magnetic field is not sufficient, electrons might be registered at different locations than they were generated, leading to a deformation of the measured profiles with respect to the beam profile.

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