Explaining Neural Networks on the Sky: Machine Learning Interpretability for Cosmic Microwave Background Maps

Abstract

We present a framework for cosmological model selection using Neural Networks (NNs) trained directly on simulated Cosmic Microwave Background (CMB) temperature and polarisation maps. We also apply SHAP (Shapley Additive exPlanations) as an interpretability approach to map the network's decision-making process. By introducing a galactic mask as a diagnostic control, we obtain attribution maps that correctly exclude this region and confirm that the classification is driven exclusively by the unmasked sky. Ultimately, these maps verify that the architecture distinguishes between $\Lambda$CDM and the non-standard feature model by evaluating the global statistical variance distributed across the valid cosmological regions. We describe the generation of Planck-like CMB maps, and the implemented hybrid architecture that combines principal component analysis and NNs, optimised for classification tasks. This work serves as a follow-up to previous analyses at the level of summary statistics and as a proof-of-concept for using machine learning and interpretability to evaluate the global statistical properties of masked CMB data. This diagnostic Machine Learning framework has the potential to enhance future detection of nontrivial inflationary signals and improve cosmological model discrimination, while also introduces the Open Science project SkyExplain, providing public access to the full pipeline for simulation, training, and interpretability of CMB map-based neural networks.