2026-06-27T23:52:45Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/1365082026-05-04T23:57:22Zcom_20.500.14352_14col_20.500.14352_15

Marín Díaz, Gabriel Rodriguez-Rodriguez, Alvaro Manuel Andrés Núñez, Eva María 2026-05-04T11:26:56Z 2026-05-04T11:26:56Z 2026-04-30 Marín Díaz, G., Rodriguez-Rodriguez, A. M., & Andrés Núñez, E. M. (2026). A Fuzzy and Explainable AI Framework for Comparing Physical and Perceptual Representations in Galaxy Morphology. AI, 7(5), 159. https://doi.org/10.3390/ai7050159 2673-2688 10.3390/ai7050159 https://hdl.handle.net/20.500.14352/136508 https://doi.org/10.3390/ai7050159 https://www.mdpi.com/2673-2688/7/5/159 Galaxy morphology combines measurable structural properties with subjective visual interpretation, limiting strictly hard-label classifications. This study proposes a framework designed to compare physically derived and human-based galaxy classifications while explicitly accounting for uncertainty and interpretability. Using photometric and structural features from the Sloan Digital Sky Survey (SDSS), physical groupings are obtained through Fuzzy C-Means clustering, enabling gradual transitions via soft memberships. Human clusters are constructed from Galaxy Zoo 2 debiased vote fractions, capturing aggregated perceptual judgments. Supervised models are trained to predict both physical and human cluster assignments from the same set of physical variables, providing a quantitative assessment of structural coherence and perceptual–physical alignment. SHAP-based explainability identifies the relative influence of color and concentration parameters in each scheme. Results show that physical clustering is driven by structural concentration and bulge dominance, while human classification exhibits smoother decision boundaries and greater sensitivity to photometric appearance. Discrepancies concentrate in transitional and orientation-sensitive systems. An interactive visualization layer supports traceable qualitative inspection. The framework provides a reproducible methodology for analyzing classification consistency, uncertainty, and human–model alignment. eng open access A Fuzzy and Explainable AI Framework for Comparing Physical and Perceptual Representations in Galaxy Morphology journal article