Galaxy Morphology Classification: Uncertainty Modeling and Out of Distribution Detection
We present a comprehensive framework for galaxy morphology classification that combines enhanced ``out-of-distribution (OOD)'' detection with improved uncertainty quantification. Using ``Galaxy Zoo DECaLS'', we trained a ResNet-34 architecture under three configurations: standard cross-entropy loss as a baseline, IsoMaxPlus loss function for OOD detection, and hybrid IsoMaxPlus+MonteCarlo Dropout for enhanced uncertainty quantification. IsoMaxPlus replaces the conventional SoftMax logits with distance-based class representations, preserving inter-class separability and enabling reliable OOD detection, without requiring additional architectural modifications or hyperparameter tuning. Coalescing IsoMaxPlus with MC Dropout provides a fast Bayesian approximation by performing multiple stochastic forward passes during inference. Our results show that IsoMaxPlus substantially improves OOD detection, increasing TNR@TPR95 by nearly $90\%$ relative to the cross-entropy baseline, while maintaining a competitive accuracy of 97\% across nine morphological classes. Additionally, with MC Dropout, the model yields more stable predictions and a reduction in calibration error, providing more reliable uncertainty estimates. The Expected Calibration Error (ECE) is reduced from $0.0095$ to $0.0026$ ($\approx73\%$) for IsoMaxPlus and $0.0033$ ($\approx65\%$) when combined with MC Dropout, compared to the baseline. Misclassified or underconfident predictions exhibit higher predictive entropy and lower minimum distance scores, providing an interpretable metric for identifying unreliable predictions. These methods are well-suited for current and upcoming large-scale surveys, where reliable automated morphological classification and awareness of uncertainty are essential for identifying rare or previously unseen galaxy morphologies.