Details of Research Outputs

In this study, a deep learning-based galaxy classification method is discussed. The classification of galaxies is important for understanding the formation and evolution of the universe, and traditional classification methods rely on artificial visual analysis, but in the face of large amounts of data, this method is time-consuming and prone to error. In recent years, automated classification methods, especially using deep learning techniques, have gradually come into focus. Deep learning models, particularly convolutional neural networks (CNNS), are capable of automatically extracting and learning complex features in galactic images, enabling efficient and accurate classification. The research plan is to integrate multimodal data, train models with large-scale datasets, and introduce interpretative analysis into the classification process to improve model transparency. Ultimately, the goal is to develop an efficient galactic classification system to support data processing and analysis in the field of astronomy.