Detection of reactor neutrinos using gadolinium-doped plastic scintillators enhanced by machine learning techniques

Abstract In this study, we present a detailed approach for detecting reactor neutrinos by employing gadolinium-doped plastic scintillators combined with machine learning techniques. The proposed detector design was simulated using the Geant4 framework, featuring segmented modules of plastic scintillators doped with gadolinium to enhance neutron capture efficiency. Inverse beta decay (IBD) events generated by ERNIE and background events produced by cosmic ray simulations were used to train and test an Extreme Gradient Boosting (XGBoost) model for signal-background discrimination. The model demonstrated high discrimination accuracy for prompt IBD events but encountered challenges with delayed neutron background discrimination due to similarities in neutron capture. Performance comparisons with traditional cut-based analyses highlighted the improved accuracy achieved through machine learning, particularly when utilizing additional event features. This work establishes the potential of gadolinium-doped plastic scintillators and machine learning in enhancing neutrino detection and lays the groundwork for future experimental validation and detector optimization.