Cosmological Parameter Estimation with a Joint-Likelihood Analysis of the Cosmic Microwave Background and Big Bang Nucleosynthesis

Abstract

We present the first joint-likelihood analysis of Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) data. Bayesian inference is performed on the baryon abundance and the effective number of neutrino species, $N_{\mathrm{eff}}$, using a CMB Boltzmann solver in combination with LINX, a new flexible and efficient BBN code. We marginalize over Planck nuisance parameters and nuclear rates to find $N_{\mathrm{eff}}=3.08_{-0.13}^{+0.13},2.94_{-0.16}^{+0.16},$ or $2.98_{-0.13}^{+0.14}$, for three separate reaction networks. This framework enables robust testing of the Lambda Cold Dark Matter paradigm and its variants with CMB and BBN data.