Identifying Warped Galaxies in Pan-STARRS and Euclid using Deep Convolutional Neural Network

Abstract: Warped galactic discs are common, yet their detection remains challenging, as the outskirts of galaxies are typically faint. Advances in deep imaging surveys improve the detectability of such features, while machine learning enables efficient analysis of large datasets. Using the Pan-STARRS EGIPS catalogue, we develop a deep learning framework by fine-tuning the Zoobot convNext-nano model on 1000 edge-on galaxy FITS images to distinguish warped and non-warped edge-on galaxies with 83\% accuracy. The trained model is then applied to a larger sample, identifying 2088 warped and 1398 non-warped galaxies with a high prediction probability threshold ($\geq 0.85$). Additionally, we use the model to predict on 3226 edge-on galaxies from the Euclid Q1 survey, demonstrating the model's ability to generalise across datasets with differing resolutions. To analyse the model predictions, we employ LayerCAM to identify the regions of galaxy images that contribute to the classification. We find that warped galaxies differ primarily in their structural properties, exhibiting lower axis ratios and higher asymmetry. Warped galaxies were found to be bluer, with younger stellar populations and enhanced star formation. These results highlight the effectiveness of deep learning methods in identifying subtle morphological features, such as warps, and demonstrate their potential for studying structural properties of galaxies in current and upcoming large imaging surveys.