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.

CONNIE experiment installed 12 charge- coupled devices (CCDs) sensors near the Angra II nuclear reactor in Angra dos Reis (Brazil) aiming to detect low energy antineutrinos produced in the core of the reactor. For two years, these sensors recorded particle images catching mainly muons and other particles such as electrons and alphas that will be considered as external radioactive background that must be removed. The images were created from the data taken every 3 hours, generating in this way a vast catalog of detected events on each CCD. In this work we propose an instance segmentation and a classification model in order to study the variation of the muon rate produced by those particles. For this purpose we developed two models: a Convolutional Neural Network (CNN) for event classification, and an instance segmentation model for identifying overlapped events. The classification model demonstrated exceptional efficacy, achieving an impressive accuracy of 0.8. Furthermore, precision and recall values of 0.85 and 0.92, respectively, were achieved for the particles of interest. Within the domain of bounding box detection, our model exhibited remarkable recall and precision rates of 71 % and 69 %, respectively, further underlining its adeptness in accurately localizing objects. Our results not only illuminate the potential of these models but also contribute to a deeper understanding of muon rate variations within the experimental context of the CONNIE setup. Index Terms- muon, deeplarning, yolo V8, yolo, CONNIE, neutrine.