Abstract
Abstract
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 ± 1.2× 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.