Speaker
Description
Resistive Plate Chambers (RPCs) are widely used as tracking detectors in high-energy physics experiments due to their simplicity, robustness, and excellent timing performance. However, low-resistive bakelite RPC prototypes often exhibit secondary hit components that degrade time and position resolution and introduce background, complicating track reconstruction. In this work, we present a data-driven approach to identify and suppress such background clusters using supervised machine-learning techniques. A set of fifteen compact, physically motivated cluster-level observables–capturing statistical and shape-related
properties of time and ADC distributions–is constructed from controlled laboratory measurements with a single-gap RPC operated under a three-scintillator coincidence trigger.
Three classification models–Deep Neural Network (DNN), one-dimensional Convolutional Neural Network (1D-CNN), and XGBoost–are trained and evaluated under identical conditions.
All models achieve efficient signal–background separation, with XGBoost providing the most stable performance. Feature-importance analysis indicates that cluster size and temporal-shape parameters are the most discriminating observables. The proposed method is computationally
efficient and relies only on compact cluster descriptors, making it well suited for integration into real-time or near real-time reconstruction pipelines in high-rate experiments.