Improving the detection efficiency of IRAND based on Convolutional Neural Network (CNN)

"CNN-Based Automated Event-Type Prediction and Test Dataset Generation for Separating Reactor Antineutrino and Cosmic-Muon Signals" This repository contains the enhanced version of the IRAND-Sim-02 simulation package, developed for the automatic generation of test datasets and the classification of antineutrino and cosmic-muon events. The original version (published previously (Mousavi, Mahdieh Sadat; Rahmani, Faezeh (2025), “IRAND-Sim-02”, Mendeley Data, V1, doi: 10.17632/vjtk7pycn2.1)) supported two source modes: **antineutrino-only** and **cosmic-muon-only**. In this extended version, a **combined source mode** has been added, enabling mixed event generation with user-defined probabilities — a key innovation for automated benchmarking, deep learning evaluation, and detector studies. A real-time connection between **Geant4** and **Python** is implemented through a TCP socket to enable online analysis, automated image generation, and (optionally) automatic event-type prediction using a trained CNN model.基于卷积神经网络（CNN）的反应堆反中微子与宇宙缪子信号分离、事件类型自动预测及测试数据集生成 本仓库收录了IRAND-Sim-02仿真软件包的增强版本，该软件包专为自动生成测试数据集并实现反中微子与宇宙缪子事件分类而开发。 此前已发布的原始版本（引用详情：Mousavi, Mahdieh Sadat; Rahmani, Faezeh (2025), “IRAND-Sim-02”, Mendeley Data, V1, doi: 10.17632/vjtk7pycn2.1）支持两种源工作模式：**仅反中微子模式**与**仅宇宙缪子模式**。 在本次增强版本中，新增了**组合源模式**，可支持以用户自定义的概率生成混合事件——这一关键创新可用于自动化基准测试、深度学习模型评估以及探测器相关研究。 本实现通过TCP套接字建立**Geant4**与**Python**之间的实时连接，以支持在线分析、自动化图像生成，以及（可选）使用已训练好的CNN模型完成事件类型自动预测。