Application of Machine Learning for the IPM-Based Profile Reconstruction

One of the most reliable devices to measure the transverse beam profile in hadron machines is Ionization Profile Monitor (IPM). This type of monitor can work in two modes: collecting electrons or ions. Typically, for lower intensity beams, the ions produced by ionization of the rest gas are extracted towards a position-sensitive detector. Ion trajectories follow the external electric field lines, however the field of the beam itself also affects their movement leading to a deformation of the observed beam profile. Correction methods for this case are known. For high brightness beams, IPM configuration in which electrons are measured, is typically used. In such mode, an external magnetic field is often applied in order to confine the transverse movement of electrons. However, for extreme beams, the distortion of the measured beam profile can still be present. The dynamics of electron movement is more complex than in case of ions, therefore the correction of the profile distortion is more difficult. Investigation of this problem using a dedicated simulation tool and machine learning algorithms lead to a beam profile correction methods for electron-collecting IPMs