标题： 宇宙参数估计：宇宙微波背景和大爆炸核合成的联合似然分析 显示英文标题

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

摘要： 我们首次对大爆炸核合成（BBN）和宇宙微波背景（CMB）数据进行了联合似然性分析。通过结合CMB玻尔兹曼求解器与LINX（一种新的灵活且高效的BBN代码），对重子丰度和有效中微子种类数 $N_{\rm eff}$ 进行了贝叶斯推断。我们对普朗克噪声参数和核反应率进行了边缘化处理，从而得到 $N_{\rm{eff}} = 3.08_{-0.13}^{+0.13},\,2.94 _{-0.16}^{+0.16},$ 或 $2.98_{-0.13}^{+0.14}$，针对三种不同的反应网络。这一框架能够稳健地检验Lambda冷暗物质模型及其变体与CMB和BBN数据的一致性。 显示英文摘要

摘要： 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, $N_{\rm eff}$, 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 $N_{\rm{eff}} = 3.08_{-0.13}^{+0.13},\,2.94 _{-0.16}^{+0.16},$ 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.