Space-charge distortion of transverse profiles measured by electron-based ionization profile monitors and correction methods

Abstract

Measurements of transverse profiles using Ionization Profile Monitors (IPMs) for high brightness beams are affected by the electromagnetic field of the beam. This interaction may cause a distortion of the measured profile shape despite strong external magnetic field applied to impose limits on the transverse movement of electrons. The mechanisms leading to this distortion are discussed in detail. The distortion itself is described by means of analytic calculations for simplified beam distributions and a full simulation model for realistic distributions. Simple relation for minimum magnetic field scaling with beam parameters for avoiding profile distortions is presented. Further, application of machine learning algorithms to the problem of reconstructing the actual beam profile from distorted measured profile is presented. The obtained results show good agreement for tests on simulation data. The performance of these algorithms indicate that they could be very useful for operations of IPMs on high brightness beams or IPMs with weak magnetic field.

Machine Learning Applied to the Analysis of Nonlinear Beam Dynamics Simulations for the CERN Large Hadron Collider and Its Luminosity Upgrade

“…The corresponding values DA i = DA(N i ) have been generated by means of the GP and then a fit of the DA data (numerical plus synthetic points) was performed using model (14b), and this procedure was repeated 5 × 10 5 times, each time computing the Mean Square Error (MSE) . Note that, indeed, two variants have been tested, namely using three fit parameters (ρ, µ, κ), or two (ρ, κ), in which µ was expressed as a function of ρ, κ according to Equation (15). It is worth mentioning that whenever the GP is used, the MSE of the fitted model is computed disregarding the synthetic points, i.e., using only the points obtained from the DA simulations.…”

“…Beam diagnostics and beam control systems were among the first domains in which ML applications were applied. This occurred already a few decades ago [11,12], although only recently, substantial progress has been made (see, e.g., [13][14][15][16][17][18] and references therein, for a sample of recent applications of ML to accelerator physics topics). The growing number of conferences and workshops that focus on ML applications in accelerator physics is a clear sign of the warm interest of that community.…”

Beam Size Reconstruction from Ionization Profile Monitors

“…The two most common types of IPMs are distinguished by the use or no-use of a guiding magnetic field parallel to extracting electric field. Physics principles, advantages and disadvantages of the IPMs with a magnetic field are discussed in [12]. This paper deals mostly with the physics principles and beam profile reconstruction in the IPMs operating with only an electric guiding field -those are used more widely because of no-need of external magnets and, therefore, smaller size, simpler design and lower cost (see Fig.…”

Application of machine learning in beam optics measurements and corrections

“…The model uses simulated cathode images, solenoid strengths and the gun phase as inputs and produces a prediction for various downstream beam parameters. Application of neural networks can be found also in correction of distorted beam profiles measured at ionization profile monitors (IPM) [75]. A neural networks-based model has been trained on IPM simulations in order to establish the mapping between measured profiles together with bunch length and bunch intensity to the original beam profile.…”