Inference products for "Finite inflation in curved space"

These are the MCMC and nested sampling inference products and input files that were used to compute results for the paper "Finite inflation in cuved space" by L. T. Hergt, F. J. Agocs, W. J. Handley, M. P. Hobson, and A. N. Lasenby from 2022. Example plotting scripts (as \(\texttt{.ipynb}\) or as \(\texttt{.html}\) files) and figures from the paper are included to demonstrate usage. We used the following python packages for the genertion of MCMC and nested sampling chains: Package Version anesthetic 2.0.0b12 classy 2.9.4 cobaya 3.0.4 GetDist 1.3.3 primpy 2.3.6 pyoscode 1.0.4 pypolychord 1.20.0 Filename conventions: \(\texttt{mcmc}\): MCMC run \(\texttt{pcs#d####}\): PolyChord run (in synchronous mode) with \(\texttt{#d}\) repeats per parameter block (where \(\texttt{d}\) is the number of parameters in that block) and with \(\texttt{####}\) live points. \(\texttt{_cl_hf}\): Using Boltzmann theory code CLASS with nonlinearities code halofit. \(\texttt{_p18}\): Using Planck 2018 CMB data. \(\texttt{_TTTEEE}\): Using the high-l TTTEEE likelihood. \(\texttt{_TTTEEElite}\): Using the lite version of the high-l TTTEEE likelihood. \(\texttt{_lowl_lowE}\): Using the low-l likelihoods for temperature and E-modes. \(\texttt{_BK15}\): Using data from the 2015 observing season of Bicep2 and the Keck Array. \(\texttt{lcdm}\): Concordance cosmological model called LCDM (standard 6 cosmological sampling parameters, no tensor perturbations, zero spatial curvature) \(\texttt{_r}\): Extension with a variable tensor-to-scalar ratio \(r\). \(\texttt{_omegak}\): Extension with a variable curvature density parameter \(\Omega_K \). \(\texttt{_H0}\): Sampling over \(H_0\) instead of \(\theta_\mathrm{s}\). \(\texttt{_omegakh2}\): Extension with a variable curvature density parameter, but sampling over \(H_0\) instead of \(\theta_\mathrm{s}\) and over \(\omega_K\equiv\Omega_Kh^2\) instead of \(\Omega_K \). \(\texttt{_mn2}\): Using a quadratic monomial potential for the computation of the primordial universe. \(\texttt{_nat}\): Using the natural inflation potential for the computation of the primordial universe. \(\texttt{_stb}\): Using the Starobinsky potential for the computation of the primordial universe. \(\texttt{_AsfoH}\): Using the primordial sampling parameters {`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`}. \(\texttt{_perm}\): Assuming a permissive reheating scenario.本数据集为2022年由L. T. Hergt、F. J. Agocs、W. J. Handley、M. P. Hobson及A. N. Lasenby共同发表的论文《弯曲空间中的有限暴胀》（"Finite inflation in curved space"）计算结果所使用的马尔可夫链蒙特卡洛（Markov Chain Monte Carlo, MCMC）与嵌套抽样推断产物及输入文件。 本文档同时收录了来自该论文的示例绘图脚本（格式为 exttt{.ipynb}或 exttt{.html}）与配套图表，用于演示数据集的使用方法。 我们在生成MCMC与嵌套抽样链的过程中，使用了以下Python软件包： | 软件包 | 版本 | |--------|------| | anesthetic | 2.0.0b12 | | classy | 2.9.4 | | cobaya | 3.0.4 | | GetDist | 1.3.3 | | primpy | 2.3.6 | | pyoscode | 1.0.4 | | pypolychord | 1.20.0 | ### 文件命名规则： 1. exttt{mcmc}：代表标准MCMC运行任务 2. exttt{pcs#d####}：代表同步模式下的PolyChord运行任务，其中`#d`表示每个参数块的重复次数（`d`为该参数块内的参数总数量），`####`表示活跃点（live points）的数量 3. exttt{_cl_hf}：采用玻尔兹曼理论代码CLASS与非线性性计算代码halofit进行理论建模 4. exttt{_p18}：采用普朗克（Planck）2018年宇宙微波背景（CMB）观测数据集 5. exttt{_TTTEEE}：采用高角分辨率TTTEEE似然函数 6. exttt{_TTTEEElite}：采用高角分辨率TTTEEE似然函数的精简版本 7. exttt{_lowl_lowE}：采用低角分辨率温度与E模式似然函数集 8. exttt{_BK15}：采用Bicep2与凯克阵列（Keck Array）2015年观测季的观测数据 9. exttt{lcdm}：代表一致性宇宙学模型ΛCDM（即标准6参数宇宙学采样模型，无张量扰动，空间曲率为零） 10. exttt{_r}：引入可变张量-标量比(r)的模型扩展 11. exttt{_omegak}：引入可变曲率密度参数(Omega_K)的模型扩展 12. exttt{_H0}：以哈勃常数(H_0)作为采样参数，而非原初声视界角( heta_mathrm{s}) 13. exttt{_omegakh2}：引入可变曲率密度参数的模型扩展，但采样参数替换为(H_0)（而非( heta_mathrm{s})）与(omega_KequivOmega_Kh^2)（而非(Omega_K)） 14. exttt{_mn2}：采用二次单项式势函数计算原初宇宙演化过程 15. exttt{_nat}：采用自然暴胀势函数计算原初宇宙演化过程 16. exttt{_stb}：采用Starobinsky势函数计算原初宇宙演化过程 17. exttt{_AsfoH}：采用原初宇宙采样参数集合{`logA_SR`, `N_star`, `f_i`, `omega_K`, `H0`} 18. exttt{_perm}：假设采用宽松型再加热场景