Evaluating extensions to LCDM: an application of Bayesian model averaging and selection

Understand

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 $\Lambda$CDM model, considering a varying curvature density parameter $\Omega_{\rm k}$, a spectral index $\mathrm{n}_{\rm s}=1$ and a varying $n_{\rm run}$, 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 $N_{\rm eff}$.
• 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.