Speaker
Description
A key measurement in CERN accelerators for beam diagnostics is transverse size. The Beam Gas Ionization (BGI) instrument enables non-destructive observation of transverse beam profiles by detecting free electrons produced through beam-gas ionization using a Timepix-family detector. However, BGI profiles often suffer from artifacts, such as beam losses, which degrade profile quality and significantly hinder analysis. This contribution addresses the challenge of background removal through machine learning techniques, employing both supervised and unsupervised approaches to enhance the accuracy and reliability of BGI profile measurements. Additionally, aspects such as the performance and time complexity of these methods are analyzed to ensure the instrument remains fully operational.
