Using machine learning techniques for BGI-based profile measurements at the CERN PS

The Beam Gas Ionization (BGI) instrument provides a non-destructive method for monitoring transverse beam profiles by detecting free electrons produced during beam-gas ionization. Utilizing a Timepix-family detector, the BGI setup at the CERN Proton Synchrotron (PS) includes two instruments dedicated to horizontal and vertical plane measurements. However, the quality of these measurements is often compromised by artifacts, such as beam losses, which degrade profile quality, make the analysis significantly more difficult and ultimately affect the instrument performance. To address these challenges, this contribution explores the application of machine learning techniques for effective background removal. Both supervised and unsupervised approaches are evaluated on data acquired from the operational systems to improve the accuracy and reliability of the reconstructed profiles. Furthermore, the computational performance and time complexity of these methods are evaluated to ensure that the proposed solutions are compatible with the operational requirements of the BGI system.