Systematic evaluation of post-hoc explainability across four CNN architectures
Deep learning classifiers are now standard tools for automated galaxy morphology analysis at the scale of next-generation astronomical surveys like Rubin LSST. However, black-box neural networks present a critical trust problem for scientific use — astronomers cannot verify whether predictions rely on genuine morphological features or spurious correlations. This project conducts a systematic evaluation of four post-hoc explainability methods (Grad-CAM, LIME, Integrated Gradients, GradientSHAP) across four CNN architectures on two galaxy morphology datasets, using quantitative faithfulness metrics to show that no single explanation method dominates universally.
Select a galaxy type, architecture, and XAI method. Run the explainer to overlay attention heatmaps and see faithfulness scores.