Seleção e análise de identificabilidade de modelos cosmológicos

Abstract

The CDM model has been widely successful in describing the structure and evolution of the Universe. However, discrepancies in fundamental cosmological parameters have motivated the investigation of alternative models, including those that consider interactions between dark matter and dark energy, as well as the inclusion of a spatial curvature parameter . The relative quality of these models can be assessed using information criteria such as AIC and BIC, which balance parametric complexity and data fitting. The results support the effectiveness of this method, as they indicate that the flat CDM model remains the most statistically favored, while more complex models tend to be penalized without significant improvements in fit quality. Beyond statistical comparisons, identifiability analysis plays a crucial role in assessing the robustness of cosmological models by verifying whether parameters can be uniquely estimated, thereby avoiding interpretative ambiguities. Despite its relevance, identifiability remains underexplored in cosmology, where parametric correlations often hinder the distinction between different model parameterizations. The systematic incorporation of this analysis is essential for the study of more complex models, where parameters are more intricately linked.