Galaxy Morphological Classification with Deformable Attention Transformer

Galaxy morphological classification is an important but challenging task in astronomy. Most prior work study coarse-level morphological classification and use raster low-dynamic range images, but we are interested in high-dynamic range images commonly produced in imaging surveys. To tackle this problem, first we build a dataset with high dynamic range for fine-level multi-class classification that are even challenging to human eyes. Then we propose to use Deformable Attention Transformer for this difficult task with five-bands images and masks, and in the experimental results our model achieves about 70% and 94% for top-1 and top-2 test set accuracies, respectively. We also visualize attention maps and analysis the results with respect to different classes and mask sizes to understand the data and behavior of the model. We confirm that our model has similar confusion patterns in confusion matrix as human along with attention visualization for capturing morphological characteristics.