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.

Surface defect identification is an essential task in the industrial quality control process, in which visual checks are conducted on a manufactured product to ensure that it meets quality standards. The convolutional neural network (CNN)-based surface defect identification method has proven to outperform traditional image processing techniques. However, the real-world surface defect datasets are limited in size due to the expensive data generation process and the rare occurrence of defects. To address this issue, this article presents a method for exploiting auxiliary information beyond the primary labels to improve the generalization ability of surface defect identification tasks. Considering the correlation between pixel-level segmentation masks, object-level bounding boxes, and global image-level classification labels, we argue that jointly learning features of the related tasks can improve the performance of surface defect identification tasks. This article proposes a framework named Defect-Aux-Net, based on multitask learning with attention mechanisms that exploit the rich additional information from related tasks with the goal of simultaneously improving robustness and accuracy of the CNN-based surface defect identification. We conducted a series of experiments with the proposed framework. The experimental results showed that the proposed method can significantly improve the performance of state-of-the-art models while achieving an overall accuracy of 97.1%, Dice score of 0.926, and mean average precision of 0.762 on defect classification, segmentation, and detection tasks.