Galaxy morphology classification: Uncertainty modeling and out of distribution detection — E8 Intelligence Research

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 (Isotropy Maximization Plus) loss function for OOD detection, and hybrid IsoMaxPlus+Monte Carlo (MC) 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-e Author: Andrew Stewart Caldin, Independent Researcher, UK. Part of the E8 Intelligence Research series. Platform: e8intelligence.com