Reconstruction of Neutron Spectra Using Silicon Carbide Detectors in Monoenergetic Fields with Machine Learning Approach

Recent advancements in machine learning have shown significant promise in nuclear applications, particularly in optimizing reactor operations, core design, and improving neutron spectroscopy techniques. This study introduces a novel approach that leverages a non-parametric and probabilistic Bayesian learning method to predict neutron spectra based on data obtained from solid-state detectors, with a specific focus on the performance of 4H-polytype Silicon Carbide detectors under various monoenergetic neutron fields. The experimental measurements were conducted at the Van de Graaff accelerator facility in Prague and at the TOTEM facility in Cadarache. In the first installation, the Silicon Carbide detector was exposed to multiple monoenergetic neutron beams generated via p+T, D+T, and D+D reactions. In the second infrastructure, the detector was subjected to a monoenergetic beam generated by GENIE16 D-T neutron generator. The measurements were post-processed to collect the deposited energy in the detector which is used as input for the machine learning algorithm in order to obtain the reconstruction of neutron spectra. Preliminary findings demonstrate that the machine learning-based approach can successfully reconstruct monoenergetic normalized neutron spectra with good accuracy, showcasing its potential for improving neutron spectroscopy with semiconductor sensors.