  This research investigates the separation of Cherenkov and Scintillation
light signals within a simulated Water-based Liquid Scintillator (WbLS)
detector, utilizing the XGBoost machine learning algorithm. The simulation data
were gathered using the Rat-Pac software, which was built on the Geant4
architecture. The use of the WbLS medium has the capability to generate both
Scintillation and Cherenkov light inside a single detector. To show the
separation power of these two physics events, we will use the supervised
learning approach. The assessment utilized a confusion matrix, classification
report, and ROC curve, with the ROC curve indicating a performance result of
$0.96 \pm 1.2\times 10^{-4}$. The research also aimed to identify essential
parameters for effectively distinguishing these physics events through machine
learning. For this, the study also introduced the SHAP methodology, utilizing
game theory to assess feature contributions. The findings demonstrated that the
number of hits has a significant effect on the trained model, while the mean
hit time has a somewhat smaller impact. This research advances the utilization
of AI and simulation data for accurate Cherenkov and Scintillation light
separation in neutrino detectors
