Deuterium-deuterium fusion charged particle detection using CR-39 and deep learning model

Abstract

Columbia Resin #39 (CR-39) solid-state nuclear track detectors are widely used in fusion research for detecting charged particles produced in fusion reactions. However, analyzing increasingly complex and large-scale CR-39 track images to extract meaningful information is a tedious and time-consuming process, often prone to human error and bias. To address these challenges, we developed an AI-based classification model capable of differentiating protons, tritons, and helions produced during D-D fusion, using CR-39 track images as input data. The CR-39 track images were processed and used to train a deep learning model. By preprocessing the track images for noise reduction and feature enhancement, we trained the You Only Look Once version 8 (YOLOv8) network to distinguish the three particle types with high accuracy. We designed a method that cuts large images into smaller pieces and then enlarges them again to achieve a lower density in each segmented piece to improve accuracy. The sliced images are then reconstructed using overlapping cuts and Non-Maximum Suppression (NMS) to handle the overlapping parts of the stitches. By adopting NMS, we retain only the bounding box with the highest confidence level, avoiding duplicate detections and achieving accurate counting. The proposed model achieved a classification accuracy of over 94 %, demonstrating its potential for improving automated track analysis in CR-39 detectors. Additionally, the model precisely identifies particle coordinates and counts, enabling comprehensive particle analysis. This study highlights the application of AI in track detection and classification, offering a robust solution for particle identification in CR-39 detector-based experiments.