论文标题
黑暗能源调查3年结果:测量三维聚类的重子声学振荡
Dark Energy Survey Year 3 Results: Measurement of the Baryon Acoustic Oscillations with Three-dimensional Clustering
论文作者
论文摘要
三维相关函数提供了一种有效的方法来汇总大规模结构的相关性,即使是成像星系调查。我们已经应用了预计的三维相关函数,$ξ_ {\ rm p} $来测量最初三年的黑暗能源调查数据,以测量Baryonic声学振荡(BAO)尺度。该样本由红移范围内的约700万个星系组成$ 0.6 <z {\ rm p} <1.1 $ $ 4108 \,\ mathrm {deg}^2 $。我们的理论建模包括超越高斯照片 - $ z $近似之外的真实真实红移分布的影响。为了增加测量值的信号噪声,采用高斯堆叠窗口函数代替常用的顶帽。 Using the full sample, $ D_{\rm M}(z_{\rm eff} ) / r_{\rm s} $, the ratio between the comoving angular diameter distance and the sound horizon, is constrained to be $ 19.00 \pm 0.67 $ (top-hat) and $ 19.15 \pm 0.58 $ (Gaussian) at $z_{\rm eff} = 0.835 $。约束比角相关性$ W $约束($ 18.84 \ pm 0.50 $)弱,因为BAO信号在红移中是异质的。考虑到$ 0.7 <z _ {\ rm p} <z _ {\ rm p} <1.0 $($ z _ {\ rm eff} = 0.845 $)的均匀的BAO信号子样本时,$ $ξ_{\ξ_{\ rm p} $ $ 19.80 $ 19.80 \ pm 0.67 $($ 19.80 $($ 19.84)和$ 19.84 $(p. pm pm pm p.后者比$ w $约束($ 19.86 \ pm 0.55 $)强大。我们发现$ξ_ {\ rm p} $结果对photo- $ z $错误比$ w $更敏感,因为$ξ_ {\ rm p} $保持了三维聚类信息,从而使其更容易摄影 - $ z $ noise。高斯窗口比前帽提供了更强的结果,因为前者旨在抑制低信号模式。 $ξ_ {\ rm p} $,$ w $等角统计具有自己的优点和缺点,它们彼此提供重要的交叉检查。
The three-dimensional correlation function offers an effective way to summarize the correlation of the large-scale structure even for imaging galaxy surveys. We have applied the projected three-dimensional correlation function, $ξ_{\rm p}$ to measure the Baryonic Acoustic Oscillations (BAO) scale on the first-three years Dark Energy Survey data. The sample consists of about 7 million galaxies in the redshift range $ 0.6 < z_{\rm p } < 1.1 $ over a footprint of $4108 \, \mathrm{deg}^2 $. Our theory modeling includes the impact of realistic true redshift distributions beyond Gaussian photo-$z$ approximation. To increase the signal-to-noise of the measurements, a Gaussian stacking window function is adopted in place of the commonly used top-hat. Using the full sample, $ D_{\rm M}(z_{\rm eff} ) / r_{\rm s} $, the ratio between the comoving angular diameter distance and the sound horizon, is constrained to be $ 19.00 \pm 0.67 $ (top-hat) and $ 19.15 \pm 0.58 $ (Gaussian) at $z_{\rm eff} = 0.835$. The constraint is weaker than the angular correlation $w$ constraint ($18.84 \pm 0.50$) because the BAO signals are heterogeneous across redshift. When a homogeneous BAO-signal sub-sample in the range $ 0.7 < z_{\rm p } < 1.0 $ ($z_{\rm eff} = 0.845$) is considered, $ξ_{\rm p} $ yields $ 19.80 \pm 0.67 $ (top-hat) and $ 19.84 \pm 0.53 $ (Gaussian). The latter is mildly stronger than the $w$ constraint ($19.86 \pm 0.55 $). We find that the $ξ_{\rm p} $ results are more sensitive to photo-$z$ errors than $w$ because $ξ_{\rm p}$ keeps the three-dimensional clustering information causing it to be more prone to photo-$z$ noise. The Gaussian window gives more robust results than the top-hat as the former is designed to suppress the low signal modes. $ξ_{\rm p}$ and the angular statistics such as $w$ have their own pros and cons, and they serve an important crosscheck with each other.