Scattering-Based Machine Learning Algorithms for Momentum Estimation in Muon Tomography

Muon tomography leverages the small, continuous flux of cosmic rays produced in the upper atmosphere to measure the density of unknown volumes. The multiple Coulomb scattering that muons undergo when passing through the material can either be leveraged or represent a measurement nuisance. In either case, the scattering dependence on muon momentum is a significant source of imprecision. This can be alleviated by including dedicated momentum measurement devices in the experiment, which have a potential cost and can interfere with measurement. An alternative consists of leveraging information on scattering withstood through a known medium. We present a comprehensive study of diverse machine-learning algorithms for this regression task, from classical feature engineering with a fully connected network to more advanced architectures such as recurrent and graph neural networks and transformers. Several real-life requirements are considered, such as the inclusion of hit reconstruction efficiency and resolution and the need for a momentum resolution prediction that can improve reconstruction methods.