Inelastic Neutron

Inelastic neutron scattering studies aim to understand the properties of materials by analyzing how neutrons interact with their constituent atoms, revealing information about their structure and dynamics. Current research focuses on improving data analysis techniques, particularly employing machine learning models like neural networks and boosted decision trees to enhance the accuracy and efficiency of extracting information from complex scattering data, including automating parameter extraction from multi-dimensional datasets. These advancements are significantly impacting fields like condensed matter physics and nuclear physics, enabling more precise characterization of materials and more efficient optimization of experimental designs in applications such as inertial confinement fusion.