论文标题

万神殿+数据集方差分析:协方差矩阵中的系统学?

An Analysis of Variance of the Pantheon+ Dataset: Systematics in the Covariance Matrix?

论文作者

Keeley, Ryan, Shafieloo, Arman, L'Huillier, Benjamin

论文摘要

我们研究了可用万神殿+数据集的统计数据。注意到,最佳拟合$λ$ CDM模型的$χ^2 $值很小,我们通过计算提供的协方差矩阵来量化其较小性的重要性,以计算$χλ$ CDM模型的分布$χ^2 $值,以模拟Pantheon+-like DataSet,以模拟Pantheon+-like DataSet。我们进一步研究了与拟合$λ$ CDM模型相对于Pantheon+数据集的残差分布,并注意到它们的散射少于协方差矩阵所预期的,但没有发现显着的峰度。这些结果表明,Pantheon+协方差矩阵被过高估计。对这些结果的一种简单解释是万神殿+数据中SN距离模量错误的$ \ sim $ 7 \%高估。当协方差矩阵通过从协方差矩阵的对角线术语中减去协方差矩阵时,检测到$λ$ CDM模型的最佳拟合$χ^2 $可实现1580的正常值,并且没有偏离$λ$ CDM的偏差。我们进一步量化了$λ$ CDM模型与修改的数据相对于修改数据的一致性,并使用诸如迭代平滑方法等模型无关的重建技术(例如,使用模型无关的重建技术)进行了减去协方差矩阵。我们发现标准模型与数据一致。对于$χ^2 $的这种较小性,有许多潜在的解释,例如高红移时的恶质偏见,或通过将它们添加到协方差矩阵中来考虑系统的不确定性,从而将系统的不确定性近似为统计。

We investigate the statistics of the available Pantheon+ dataset. Noticing that the $χ^2$ value for the best-fit $Λ$CDM model to the real data is small, we quantify how significant its smallness is by calculating the distribution of $χ^2$ values for the best-fit $Λ$CDM model fit to mock Pantheon+-like datasets, using the provided covariance matrix. We further investigate the distribution of the residuals of the Pantheon+ dataset with respect to the best-fit $Λ$CDM model, and notice that they scatter less than would be expected from the covariance matrix but find no significant kurtosis. These results point to the conclusion that the Pantheon+ covariance matrix is over-estimated. One simple interpretation of these results is a $\sim$7\% overestimation of errors on SN distance moduli in Pantheon+ data. When the covariance matrix is reduced by subtracting an intrinsic scatter term from the diagonal terms of the covariance matrix, the best-fit $χ^2$ for the $Λ$CDM model achieves a normal value of 1580 and no deviation from $Λ$CDM is detected. We further quantify how consistent the $Λ$CDM model is with respect to the modified data with the subtracted covariance matrix using model-independent reconstruction techniques such as the iterative smoothing method. We find that the standard model is consistent with the data. There are a number of potential explanations for this smallness of the $χ^2$, such as a Malmquist bias at high redshift, or accounting for systematic uncertainties by adding them to the covariance matrix, thus approximating systematic uncertainties as statistical ones.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源