Papers › Evaluating extensions to LCDM: an application of Bayesian model averaging and selection
Evaluating extensions to LCDM: an application of Bayesian model averaging and selection
S. Paradiso, G. McGee, W. J. Percival
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We employ Bayesian Model Averaging (BMA) as a powerful statistical framework to address key cosmological questions about the universe's fundamental properties. We explore extensions beyond the standard ΛCDM model, considering a varying curvature density parameter Ωₖ, a spectral index nₛ=1 and a varying nᵣᵤₙ, a constant dark energy equation of state (EOS) $w_0$CDM and a time-dependent one $w_0w_a$CDM. We also test cosmological data against a varying effective number of neutrino species N_(eff). Data from different combinations of cosmic microwave background (CMB) data from the last Planck PR4 analysis, CMB lensing from Planck 2018, baryonic acoustic oscillations (BAO) and the Bicep-KECK 2018 results, are used. We find that the standard ΛCDM model is favoured when combining CMB data with CMB lensing, BAO and Bicep-KECK 2018 data against $K-\Lambda$CDM model $N_{\rm eff}-\Lambda$CDM with a probability > 80%. When investigating the dark energy EOS, we find that this dataset is not able to express a strong preference between the standard ΛCDM model and the constant dark energy EOS model $w_0$CDM, with an approximately split model posterior probability of ≈60%:40% in favour of ΛCDM, whereas the time-varying dark energy EOS model is ruled out. Finally, we find that the CMB data alone show a strong preference for a model that includes the running of the spectral index nᵣᵤₙ, with a probability ≈90%, when compared to the nₛ=1 model and the standard ΛCDM. Overall, we find that including the model uncertainty in the considered cases does not significantly impact the Hubble tension.
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