Intelligent System for the Tracking of Muon Traces in the CONNIE Experiment

In the CONNIE experiment, 14 charge-coupled device (CCD) sensors record particle interactions near a nuclear reactor in Brazil. Muons, originating from cosmic rays, create background noise that can hinder the detection of neutrino interactions. This study proposes an intelligent system for re-constructing muon traces within the sensor data. To achieve this, a Convolutional Neural Network (CNN) model based on YOLOv8 was developed. Two datasets containing real experimental images were prepared: one for training the model to identify and classify muonic events, and another for calibrating an algorithm to predict muon trajectories based on the impact characteristics in the detectors. The system achieved a detection success rate exceeding 83 % for single muons in real data, demonstrating its potential for mitigating noise and enhancing particle tracking in physics experiments.