A Fuzzy and Explainable AI Framework for Comparing Physical and Perceptual Representations in Galaxy Morphology
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 a...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2026 |
| País: | España |
| Institución: | Universidad Europea (UEM) |
| Repositorio: | ABACUS. Repositorio de Producción Científica |
| Idioma: | inglés |
| OAI Identifier: | oai:dnet:abacusreposi::1d63e84b0539e211f1b2839ffafd4a38 |
| Acceso en línea: | https://hdl.handle.net/11268/17067 |
| Access Level: | acceso abierto |
| Palabra clave: | Estadística Inteligencia artificial Astronomía Lógica matemática Goal 9: Build resilient infrastructure, promote sustainable industrialization and foster innovation Goal 4: Quality education |
| Sumario: | 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. |
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