We present an extended validation of semi-analytical, semi-empirical covariance matrices for the two-point correlation function (2PCF) on simulated catalogs representative of Luminous Red Galaxies (LRG) data collected during the initial two months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). We run the pipeline on multiple extended Zel'dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and compare the results with the mock sample covariance to assess the accuracy and its fluctuations. We propose an extension of the previously developed formalism for catalogs processed with standard reconstruction algorithms. We consider methods for comparing covariance matrices in detail, highlighting their interpretation and statistical properties caused by sample variance, in particular, nontrivial expectation values of certain metrics even when the external covariance estimate is perfect. With improved mocks and validation techniques, we confirm a good agreement between our predictions and sample covariance. This allows one to generate covariance matrices for comparable datasets without the need to create numerous mock galaxy catalogs with matching clustering, only requiring 2PCF measurements from the data itself. The code used in this paper is publicly available at https://github.com/oliverphilcox/RascalC.

翻译：我们针对模拟星系目录中两点相关函数（2PCF）的半解析、半经验协方差矩阵进行了扩展验证，这些目录代表了暗能量光谱巡天仪器（DESI）第四阶段地面设施在最初两个月运行期间收集的发光红星系（LRG）数据。我们在多个经过对应样本筛选的扩展泽尔多维奇（EZ）模拟星系目录上运行技术流程，并将结果与模拟样本协方差进行比较，以评估其精度及波动性。我们提出对先前开发的、适用于经标准重建算法处理的目录的形式化方法进行扩展。我们详细考虑了协方差矩阵的比较方法，强调其因样本方差引起的统计特性与解释——特别是当外部协方差估计完美时，某些度量仍存在非平凡期望值。通过改进的模拟与验证技术，我们确认预测结果与样本协方差之间具有良好一致性。这使得我们无需生成大量具有匹配成团性的模拟星系目录，仅需利用数据本身的2PCF测量值即可为可比数据集生成协方差矩阵。本文所用代码已公开于https://github.com/oliverphilcox/RascalC。