Bayesian evidence for the tensor-to-scalar ratio r and neutrino masses m_nu: Effects of uniform vs logarithmic priors (supplementary inference products)

These are the nested sampling inference products and input files that were used to compute results for arXiv:2102.11511. Example plotting scripts (as .ipynb or as .html files) and figures from the papers are included to demonstrate usage. Filename conventions: lcdm: Concordance cosmological model called \(\Lambda\mathrm{CDM}\) (without extension this assumes \(r=0\) and a single massive neutrino with mass \(m_\nu=0.06\,\mathrm{eV}\)). _r: \(\Lambda\mathrm{CDM}\) with variable tensor-to-scalar ratio \(r\). _nu: \(\Lambda\mathrm{CDM}\) with three massive neutrinos, sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\). mcmc: Cobaya's Markov Chain Monte Carlo Metropolis sampler.https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 pc#d###: PolyChord run with #d repeats per parameter block (where d is the number of parameters in that block) and with ### live points.https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 _class: theory code CLASS.https://github.com/lesgourg/class_public/releases/tag/v2.9.4 _p18_TTTEEElowTE_SZ: Planck 2018 TT,TE,EE+lowl+lowE data.https://pla.esac.esa.int/pla/#cosmology _nufit50: NuFIT 5.0 data.http://www.nu-fit.org/?q=node/228 _NH and _IH: normal and inverted neutrino hierarchy. _logr##: logarithmic sampling of tensor-to-scalar ratio \(r\) with lower log bound given .by log10r=-##. _mdD: sampling over the lightest neutrino mass \(m_\mathrm{light}\) and the squared mass splittings \(\delta m^2\) and \(\Delta m^2\) (medium and heavy neutrino mass are derived parameters) with mass units in eV. _logmdD##: logarithmic (instead of uniform) sampling of the lightest neutrino mass \(m_\mathrm{light}\) with lower log bound given by log10mlight=-##. Datasets used for the nested sampling runs: Planck 2018 TT,TE,EE+lowl+lowE: https://pla.esac.esa.int/pla/#cosmology NuFIT 5.0: http://www.nu-fit.org/?q=node/228 Software used: Cobaya: https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 CLASS: https://github.com/lesgourg/class_public/releases/tag/v2.9.4 PolyChord: https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 Anesthetic: https://github.com/lukashergt/anesthetic/tree/138299739544e888cc318746be087c898f1aff15 For more details see Cobaya's (https://cobaya.readthedocs.io/en/latest/index.html) and Anesthetic's (https://anesthetic.readthedocs.io/en/latest/) documentation.本数据集包含用于计算arXiv:2102.11511论文结果的嵌套抽样推断产物与输入文件。 附带了示例绘图脚本（格式为.ipynb或.html）与论文原图，用于演示使用方法。 文件名约定： lcdm：指代ΛCDM（Lambda Cold Dark Matter，即一致性宇宙学模型），未指定扩展名时默认张量-标量比r=0，且包含单个质量为m_ν=0.06 eV的有质量中微子。 _r：包含可变张量-标量比r的ΛCDM模型。 _nu：包含三个有质量中微子的ΛCDM模型，对最轻中微子质量m_light以及平方质量分裂δm²和Δm²进行参数抽样。 mcmc：采用Cobaya开发的马尔可夫链蒙特卡洛-梅特罗波利斯（Markov Chain Monte Carlo Metropolis）采样器，对应版本发布页面：https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 pc#d###：PolyChord运行配置，其中#d为每个参数块的重复次数（d为该参数块包含的参数数量），###为活点数，对应版本发布页面：https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 _class：理论计算代码CLASS，对应版本发布页面：https://github.com/lesgourg/class_public/releases/tag/v2.9.4 _p18_TTTEEElowTE_SZ：采用普朗克2018年TT、TE、EE+lowl+lowE观测数据集，对应官方页面：https://pla.esac.esa.int/pla/#cosmology _nufit50：采用NuFIT 5.0观测数据集，对应官方页面：http://www.nu-fit.org/?q=node/228 _NH与_IH：分别对应正常和倒置中微子质量层级。 _logr##：对张量-标量比r进行对数抽样，其对数下限为log₁₀r=-##。 _mdD：对最轻中微子质量m_light以及平方质量分裂δm²和Δm²进行参数抽样（中微子中等质量与重质量为导出参数），质量单位为eV。 _logmdD##：对最轻中微子质量m_light进行对数（而非均匀）抽样，其对数下限为log₁₀m_light=-##。 本次嵌套抽样运行采用的数据集： 普朗克2018年TT、TE、EE+lowl+lowE数据集：https://pla.esac.esa.int/pla/#cosmology NuFIT 5.0数据集：http://www.nu-fit.org/?q=node/228 本次研究使用的软件： Cobaya：https://github.com/CobayaSampler/cobaya/releases/tag/v3.0.2 CLASS：https://github.com/lesgourg/class_public/releases/tag/v2.9.4 PolyChord：https://github.com/PolyChord/PolyChordLite/releases/tag/1.17.1 Anesthetic：https://github.com/lukashergt/anesthetic/tree/138299739544e888cc318746be087c898f1aff15 更多细节可参阅Cobaya官方文档（https://cobaya.readthedocs.io/en/latest/index.html）与Anesthetic官方文档（https://anesthetic.readthedocs.io/en/latest/）。