Master's defense: Petroula Karakosta

Using McStas Union components to simulate a magnet sample environment and predicting background with machine learning

Neutron scattering is used for the study of a wide range of material properties. Since neutrons can be easily transmitted through materials, neutron scattering allows for quite complicated sample environments with control over the sample conditions, such as temperature, as well as the presence of strong magnetic fields.
The presence of magnets in scattering experiments necessitates a significant amount of materials in the structure. The coils of the magnets, which are not in the direct beam, add more material into the structure and could influence the experiments, since neutrons would scatter multiple times before reaching the detector. Additionally, they exert large forces on the structure that need to be withstood, requiring more material to safeguard the structural integrity of the system.
In an attempt to investigate the effect of the sample environment on the resulting background scattering, simulations of elastic neutron scattering data in the presence of multiple scattering from the sample environment are carried out. A model of the 15 T magnet for the BIFROST spectrometer at ESS is constructed with the Union tool in McStas, a neutron ray-trace simulation package. Furthermore, the selection of few, yet significant input parameters is followed by parametrisation of the model, aimed at generating a substantial data set of simulation results. The objective behind this process is to construct a comprehensive training set for a machine learning model intended to predict background signal due to multiple scattering.