Speaker
Description
This work investigates the use of Generative Adversarial Networks (GANs) as a tool for data augmentation in astronomical galaxy classification. Convolutional Neural Networks (CNNs) generally perform better with large datasets, but astronomical datasets often suffer from class imbalance and limited samples for rare galaxy types. To address this, a GAN model was trained to generate synthetic galaxy images for underrepresented classes using the Galaxy10 DECaLS dataset. A CNN classifier trained on the original dataset achieved 71% accuracy, while the same CNN trained on the augmented dataset achieved 90.90% accuracy. This demonstrates the strong potential of GAN-based augmentation in improving classification accuracy for as- tronomy, medicine, and other fields with low-sample data.