Speaker
Description
Water Cherenkov detectors (WCDs) offer a robust and economical solution for real time radiation monitoring by detecting Cherenkov light from charged particles moving faster than light in water. This work presents a novel two stage classification framework for gamma-neutron discrimination: an initial physics based energy threshold filters unambiguous low energy gamma sources, followed by a machine learning ensemble that resolves ambiguities at higher energies. The detector response was characterized using $^{60}$Co (1.17/1.33 MeV), $^{137}$Cs (0.66 MeV), and a shielded $^{241}$AmBe source, with lead, paraffin, and cadmium shielding employed to isolate neutron and gamma interactions. Energy calibration established a linear ADU to MeV conversion %(R$^2$ = 0.966),
enabling identification of a neutron detection threshold at $2.62 \pm 0.77$ MeV via $3\sigma$ significance analysis. Stage one categorizes sources as pure gamma (below threshold) or neutron emitting (at threshold). For ambiguous cases above threshold, a machine learning pipeline utilizing pulse shape analysis was developed. A soft voting ensemble (Bagging, CatBoost, and MLP) achieved 0.816 accuracy and 0.921 AUC. This hybrid %"traffic light"
scheme combines physics based filtering with ML refinement, offering an interpretable and scalable solution for nuclear security, nonproliferation monitoring, and fundamental radiation research. Future work will explore deep learning architectures for waveform analysis and advanced statistical models for low energy spectra.
Key Words
WCD, Gamma-Neutron Discrimination, Machine Learning, Pulse Shape Analysis, Nuclear Security