Dual-branch network architecture used in this study. The primary branch processes the images through a series of convolutional layers (yellow), max-pooling layers (red), and dense layers (blue) to extract key features. The secondary branch processes the spatial information (pixel coordinates [x, y]) through multiple dense layers. Both branches merge through a concatenation layer (green), followed by additional dense layers and a final softmax layer for classification. This architecture allows the model to leverage image features and spatial information for more accurate star-galaxy classification.