论文标题
光晕3点相关函数：一种方法分析
The halo 3-point correlation function: a methodological analysis
论文作者
论文摘要
我们从密涅瓦模拟中测量300个光晕目录的3pcf，涵盖$ 〜1000 h^{ - 3} \ Mathrm {gpc}^3 $。每个3PCF测量都包括所有可能的三角形配置，两侧在20到$ 130H^{ - 1} \ Mathrm {gpc} $之间。首先，我们测试了协方差矩阵的不同估计值，这是分析的关键方面。我们将计算得出的协方差从有限但准确的基准模拟中计算出来，以从Pinocchio代码生成的$ 10000 $近似光晕目录获得的基准模拟。我们证明了两个数字估计的协方差矩阵在很大程度上匹配，从而确认了基于拉格朗日扰动理论的近似方法的有效性，用于生成适合协方差估计的模拟。我们还将数值协方差与高斯近似中的理论预测进行了比较。我们发现两个高于40 $ h^{ - 1} \ mathrm {gpc} $的分离之间的匹配度很好。我们在扰动理论中测试了3PCF树级模型。该模型在旨在确定偏差参数的可能性分析中采用。我们发现，对于我们在Redshift $ z = 1 $处的Halos样本的样本，Tree-level模型对于分离$ R \ GEQ 40 \，H^{ - 1} \ MATHRM {GPC} $都很好。通过这种比例切割获得的结果与协方差矩阵的不同选择是可靠的。我们与对先前出版物中已经提出的光环双光谱的类似分析进行了比较，这两个统计数据之间达成了显着的一致性。然后，我们测试不同的假设，以构建定义假设的可靠组合的模型，从而导致无偏见的参数估计。我们的结果证实了3PCF的重要性，为其包含在可能性分析中的良好配方提供了良好的配方。此外，它为进一步的改进开辟了道路，尤其是在建模方面，从非线性制度中提取信息
We measure the 3PCF of 300 halo catalogs from the Minerva simulations covering a total volume of $~1000 h^{-3} \mathrm{Gpc}^3$. Each 3PCF measurement includes all possible triangular configurations with sides between 20 and $130h^{-1}\mathrm{Gpc}$. First, we test different estimates of the covariance matrix, a crucial aspect of the analysis. We compare the covariance computed numerically from the limited but accurate benchmark simulations set to the one obtained from $10000$ approximate halo catalogs generated with the Pinocchio code. We demonstrate that the two numerically-estimated covariance matrices largely match, confirming the validity of approximate methods based on Lagrangian Perturbation Theory for generating mocks suitable for covariance estimation. We also compare the numerical covariance with a theoretical prediction in the Gaussian approximation. We find a good match between the two for separations above 40 $h^{-1} \mathrm{Gpc}$. We test the 3PCF tree-level model in Perturbation Theory. The model is adopted in a likelihood analysis aimed at the determination of bias parameters. We find that, for our sample of halos at redshift $z=1$, the tree-level model performs well for separations $r \geq 40 \, h^{-1}\mathrm{Gpc}$. Results obtained with this scale cut are robust against different choices of covariance matrix. We compare to the analogous analysis of the halo bispectrum already presented in a previous publication, finding a remarkable agreement between the two statistics. We then test different assumptions to build the model defining a robust combination of hypotheses that lead to unbiased parameter estimates. Our results confirm the importance of 3PCF, supplying a solid recipe for its inclusion in likelihood analyses. Moreover, it opens the path for further improvements, especially in modelling, to extract information from non-linear regimes
