论文标题
将电源的记忆与宇宙学的记忆分离出来的$α$森林功率谱。
Separating the memory of reionization from cosmology in the Ly$α$ forest power spectrum at the post-reionization era
论文作者
论文摘要
最近已经表明，可以通过将电离的记忆与宇宙学信息边缘化的记忆边缘化，从$α$森林功率谱中提取。宇宙电离对$α$森林功率谱系的这种影响可以在宇宙学的时间范围内生存，因为宇宙电离不均匀，而浓密区域的随后冲击可以将低密度区域的气体加热到$ \ sim 3 \ sim 3 \ sim 3 \ times10^4 $ k，并将其压缩至平均密度。然而，基于特定的电离模型的假设，边缘化对电离记忆的当前方法不仅取决于模型依赖性，而且在计算上也很昂贵。在这里，我们提出了一个简单的分析模板，以实现宇宙电离的影响，从而将其视为一种宽带系统的系统，可以通过模型独立的方式将其边缘化以从Ly $α$ Forest中从LY $α$ Forest中介绍。 This template performs remarkably well with an error of $\leq 6 \%$ at large scales $k \approx 0.19$ Mpc$^{-1}$ where the effect of the memory of reionization is important, and reproduces the broadband effect of the memory of reionization in the Ly$α$ forest correlation function, as well as the expected bias of cosmological parameters due to this systematic.当假设针对边缘化目的的特定回离模型时，该模板可以成功恢复宇宙参数空间中预测误差的形态，而对宇宙学参数的预测误差略有高估。我们进一步提出了在Ly $α$ forest 1D功率谱上的系统模板的类似模板。
It has been recently shown that the astrophysics of reionization can be extracted from the Ly$α$ forest power spectrum by marginalizing the memory of reionization over cosmological information. This impact of cosmic reionization on the Ly$α$ forest power spectrum can survive cosmological time scales because cosmic reionization, which is inhomogeneous, and subsequent shocks from denser regions can heat the gas in low-density regions to $\sim 3\times10^4$ K and compress it to mean-density. Current approach of marginalization over the memory of reionization, however, is not only model-dependent, based on the assumption of a specific reionization model, but also computationally expensive. Here we propose a simple analytical template for the impact of cosmic reionization, thereby treating it as a broadband systematic to be marginalized over for Bayesian inference of cosmological information from the Ly$α$ forest in a model-independent manner. This template performs remarkably well with an error of $\leq 6 \%$ at large scales $k \approx 0.19$ Mpc$^{-1}$ where the effect of the memory of reionization is important, and reproduces the broadband effect of the memory of reionization in the Ly$α$ forest correlation function, as well as the expected bias of cosmological parameters due to this systematic. The template can successfully recover the morphology of forecast errors in cosmological parameter space as expected when assuming a specific reionization model for marginalization purposes, with a slight overestimation of tens of per cent for the forecast errors on the cosmological parameters. We further propose a similar template for this systematic on the Ly$α$ forest 1D power spectrum.
