Investigating the suitability of data-driven methods for extracting physical parameters in cosmological models

Abstract

Recent cosmological observations have reached a level of precision that enables the determination and statistical analysis of cosmological parameters with increased accuracy. Despite the significant progress in observational data, our current understanding is still insufficient to fully elucidate the origins of dark energy and dark matter. Addressing the complexities of the observational data may necessitate the development of more sophisticated data analysis techniques or the formulation of new theoretical models. The estimation of some cosmological parameters exhibits variations depending on the chosen physical model, even when utilizing the same observational data. In order to overcome model-dependence, alternative methods such as machine learning techniques based solely on observed data are being explored. However, it is crucial to acknowledge that while this approach may provide insights into the underlying physical laws, it also carries the risk of generating entirely unphysical interpretations.

The primary objective of this article is to identify the most appropriate data-driven method for extracting physical parameters in cosmological models, with a specific focus on determining the values of two critical parameters: the Hubble constant ($H_0$) and the density parameter for dark energy (${\Omega }_{\Lambda }^0$). Our research findings demonstrate a rigorous comparison between the results derived exclusively from observational data and those predicted by the theoretical $\mathrm{\Lambda CDM}$ (Lambda Cold Dark Matter) model. Through this comparative analysis, we have successfully reaffirmed the effectiveness of the $\mathrm{\Lambda CDM}$ model in accurately describing the current observed universe.