Speaker
Jordan Ashley
(UTK)
Description
Among muon collider development efforts, beam induced background (BIB) reduction presents a significant hurdle to maximizing discovery potential. This project approaches the problem with a focus on cluster analysis aided by machine learning tools such as multivariate classification algorithms. We aim to utilize model training on geometric features of the detector in parallel with cluster properties to produce an efficient classifier capable of reliable signal versus background differentiation.
