DESI DR2 Data Favors Standard ΛCDM Cosmology Over R_h=ct Model

This research presents a comparative analysis of the standard $\Lambda$CDM cosmological model and the alternative $R_h=ct$ framework, utilizing recent observational data from cosmic chronometers (CC), Type Ia supernova, and baryon acoustic oscillations. The study evaluates the ability of each model to reproduce the observed expansion history of the Universe through a joint statistical assessment based on $\chi^2$ statistics, Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Bayes factor. The results indicate that while both models yield acceptable fits, $\Lambda$CDM consistently attains lower information-criterion values and higher likelihood, suggesting a superior overall performance. An examination of the redshift evolution of the Hubble parameter $H(z)$ and the deceleration parameter $q(z)$ reveals that $\Lambda$CDM naturally captures the transition from early-time deceleration to late-time acceleration, whereas $R_h=ct$ predicts a strictly linear expansion. The study also estimates the age of the Universe within both models, obtaining $t_0^{\Lambda CDM}= 13.676_{-0.81}^{+0.92}$Gyr and $t_0^{R_h=ct}= 16.035_{-0.98}^{+1.09}$Gyr. The posterior-derived age in the $\Lambda$CDM framework is broadly consistent with the Planck 2018 CMB result, further supporting its validity. However, the authors acknowledge that recent JWST observations of unexpectedly mature high-redshift galaxies have renewed discussion regarding the timeline of early structure formation, suggesting that fully resolving cosmic evolution may require refinements beyond the concordance paradigm. This research provides further evidence for the robustness of the $\Lambda$CDM model while also highlighting the need for continued investigation and refinement in light of new observational data.

*Transparency Disclosure: This analysis was conducted by an AI model and reviewed by human experts. The AI model is trained on a broad range of publicly available information and is designed to provide objective and unbiased assessments. However, as with any AI-driven analysis, there is a possibility of errors or omissions. Users are encouraged to consult multiple sources of information before making decisions.*

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