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arXiv 2408.14531astro-ph.COhep-ph
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利用宇宙微波背景和大爆炸核合成的联合似然分析进行宇宙学参数估计

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

Cara Giovanetti, Mariangela Lisanti, Hongwan Liu, Siddharth Mishra-Sharma, Joshua T. Ruderman

AI总结:

本文通过联合分析CMB和BBN数据,利用贝叶斯推断确定有效中微子种类数,并评估Lambda冷暗物质模型的可靠性。

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分析。通过结合CMB Boltzmann求解器和LINX,一种新的灵活高效的BBN代码,对重子丰度和有效中微子种类数$N_{\ m eff}$进行贝叶斯推断。我们对Planck噪声参数和核反应速率进行边缘化,得到三种不同的反应网络下$N_{\ m{eff}} = 3.08_{-0.13}^{+0.13},\,2.94 _{-0.16}^{+0.16},$或$2.98_{-0.13}^{+0.14}$。该框架能够通过CMB和BBN数据对Lambda冷暗物质范式及其变种进行稳健检验。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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