Constraining cosmological parameters with viscous modified chaplygin gas and generalized cosmic chaplygin gas models in Horava–Lifshitz gravity: Utilizing late-time datasets

Abstract

This study investigates accelerated cosmic expansion using the Viscous Modified Chaplygin Gas (VMMG) and Generalized Cosmic Chaplygin Gas (GCCM) within Horava–Lifshitz gravity. Our primary objective is to constrain essential cosmological parameters, such as the Hubble Parameter ($H_0$) and Sound Horizon ($r_d$). We incorporate recent datasets comprising 17 Baryon Acoustic Oscillation observations, 33 Cosmic Chronometer measurements, 40 Type Ia Supernovae data points, 24 quasar Hubble diagram data points, and 162 Gamma Ray Bursts data points. Additionally, we integrate the most recent determination of the Hubble constant (R22). We treat $r_d$ as a free parameter, which offers several advantages, including mitigating bias, enhancing precision, and improving compatibility with various datasets. Consequently, by introducing random correlations in the covariance matrix during simulation, errors are effectively reduced. Our estimated values of the Hubble constant ($H_0$) and $r_d$ consistently align with measurements from both the Planck and SDSS experiments. Additionally, cosmographic tests offer valuable insights into the dynamics of various cosmological models, enriching our understanding of cosmic evolution. Statefinder diagnostics provide deeper insights into cosmic expansion dynamics, aiding in distinguishing between both cosmological frameworks. Furthermore, the $o_m$ diagnostic test reveals that at late times, the VMMG model falls into the phantom region, while the Generalized GCCM falls into the quintessence region. Finally, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) provide support for all models under consideration, indicating that each model offers a plausible explanation. Notably, the $\Lambda$CDM model emerges with the lowest AIC score, suggesting its relatively superior fit compared to others. Additionally, validation through the reduced ${\chi }_{\text{red}}^2$ statistic confirms satisfactory fits across all models, further reinforcing their credibility in explaining the observed data.