论文标题
Planck CMB镜头与Desi样LRGS的互相关
Cross-Correlation of Planck CMB Lensing with DESI-Like LRGs
论文作者
论文摘要
宇宙微波背景（CMB）的镜头与其他大规模结构的示踪剂之间的互相关提供了一种独特的方法，可以重建暗物质的生长，宇宙学和星系物理学之间的破裂性，并测试重力的理论。我们检测从贴花成像和CMB镜头映射中选择的类似Desi的发光红星系（LRG）之间的互相关，并用Planck卫星重建，其意义为$ s/n = 27.2 $，比量表上的$ \ ell _ {\ rm min} = 30 $，$ \ el \ ell \ el _ c = 1000。要纠正放大偏见，我们确定LRG累积幅度的斜率在微弱的极限为$ s = 0.999 \ pm 0.015 $，并以$ c^{κG} _ {\ ell} _ {\ ell}，c^{gg} _ {gg} _ {\ ell} $ cromples fivess five five of $ c^{κG} _ {\ ell} _ {\ Ell} _ {\ Ell} _ { We fit the large-scale galaxy bias at the effective redshift of the cross-correlation $z_{\rm eff} \approx 0.68$ using two different bias evolution agnostic models: a HaloFit times linear bias model where the bias evolution is folded into the clustering-based estimation of the redshift kernel, and a Lagrangian perturbation theory model of the clustering evaluated at $ z _ {\ rm eff} $。我们还确定了红移分布中不确定性的偏差误差；在此错误中，这两种方法彼此相互同意，并具有DESI调查期望。
Cross-correlations between the lensing of the cosmic microwave background (CMB) and other tracers of large-scale structure provide a unique way to reconstruct the growth of dark matter, break degeneracies between cosmology and galaxy physics, and test theories of modified gravity. We detect a cross-correlation between DESI-like luminous red galaxies (LRGs) selected from DECaLS imaging and CMB lensing maps reconstructed with the Planck satellite at a significance of $S/N = 27.2$ over scales $\ell_{\rm min} = 30$, $\ell_{\rm max} = 1000$. To correct for magnification bias, we determine the slope of the LRG cumulative magnitude function at the faint limit as $s = 0.999 \pm 0.015$, and find corresponding corrections on the order of a few percent for $C^{κg}_{\ell}, C^{gg}_{\ell}$ across the scales of interest. We fit the large-scale galaxy bias at the effective redshift of the cross-correlation $z_{\rm eff} \approx 0.68$ using two different bias evolution agnostic models: a HaloFit times linear bias model where the bias evolution is folded into the clustering-based estimation of the redshift kernel, and a Lagrangian perturbation theory model of the clustering evaluated at $z_{\rm eff}$. We also determine the error on the bias from uncertainty in the redshift distribution; within this error, the two methods show excellent agreement with each other and with DESI survey expectations.
