A Machine Learning Approach to Predict the Efficiency of the Semiconductor Neutron Detector

In this research paper, we have developed a novel approach using the machine learning models to predict the efficiency of semiconductor neutron detector for boric acid based converter material. Considering the over head of computational resources and time to compute the efficiency of a neutron detector, it becomes imperative to find the models that can take much lesser resources and time. In this novel work, the model was trained from the earlier research work done on GEANT4 simulation software and the predicted data was also compared with the simulated data. The various machine learning regression model were designed for stacked detector. Further, comparison have been done to find the best fit for the experimental data.