论文标题
干涉测量HI强度映射：扰动理论预测和前景去除效应
Interferometric HI intensity mapping: perturbation theory predictions and foreground removal effects
论文作者
论文摘要
我们使用大规模结构（EFTOFLSS）的有效田间理论为HI强度映射功率谱多物提供了扰动理论预测，这应该使我们能够限制利用轻度非线性尺度的宇宙学参数。假设提议的干涉HI强度映射实验（如和弦和PUMA）的典型调查规范以及对扰动理论建模的有效性范围的现实范围，我们以$ z = 0.5 $的红色速度分析进行模拟的完整形状MCMC分析，并与Stage-IV IV-IV光学的光学星系相比。我们包括使用基于模拟的处方的21厘米前景去除的影响，并量化对参数估计的精度和准确性的影响。我们总共改变了11个参数：3个宇宙学参数，7个偏差和反对参数以及HI亮度温度。其中，感兴趣的四个参数是：冷暗物质密度，$ω_ {\ rm c} $，哈勃参数，$ h $，功率谱的原始幅度，$ a _ _ {\ rm s} $和linear hi biias hi biias，$ b_1 $。对于最佳情况，我们以$ <3 \％$ $ $ 68 \％$ pustrort Level级别的$ <3 \％$错误获得所有参数的限制。当我们包含前景删除效果时，参数估计的偏见是$ω_ {\ rm c}，h $和$ b_1 $的强烈偏见，而$ a _ {\ rm s} $的偏差较小（$ <2σ$）。我们发现，需要缩小规模$ k _ {\ rm min} \ geq 0.03 \，h/{\ mathrm {mpc}} $需要以$ω_ {\ rm c} $和$ h $的准确估计，以$ b_1 $ b_1 $ biase的价格下降，而$ω_ {\ rm c} $ {\ rm c} $和$ h $。我们评论这些结果对实际数据分析的含义。
We provide perturbation theory predictions for the HI intensity mapping power spectrum multipoles using the Effective Field Theory of Large Scale Structure (EFTofLSS), which should allow us to constrain cosmological parameters exploiting mildly nonlinear scales. Assuming survey specifications typical of proposed interferometric HI intensity mapping experiments like CHORD and PUMA, and realistic ranges of validity for the perturbation theory modelling, we run mock full shape MCMC analyses at a redshift bin centred at $z=0.5$, and compare with Stage-IV optical galaxy surveys. We include the impact of 21cm foreground removal using simulations-based prescriptions, and quantify the effects on the precision and accuracy of the parameter estimation. We vary 11 parameters in total: 3 cosmological parameters, 7 bias and counterterms parameters, and the HI brightness temperature. Amongst them, the 4 parameters of interest are: the cold dark matter density, $ω_{\rm c}$, the Hubble parameter, $h$, the primordial amplitude of the power spectrum, $A_{\rm s}$, and the linear HI bias, $b_1$. For the best case scenario, we obtain unbiased constraints on all parameters with $<3\%$ errors at $68\%$ confidence level. When we include the foreground removal effects, the parameter estimation becomes strongly biased for $ω_{\rm c}, h$, and $b_1$, while $A_{\rm s}$ is less biased ($< 2σ$). We find that scale cuts $k_{\rm min} \geq 0.03 \, h/{\mathrm{Mpc}}$ are required to return accurate estimates for $ω_{\rm c}$ and $h$, at the price of a decrease in the precision, while $b_1$ remains strongly biased. We comment on the implications of these results for real data analyses.
