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：我们针对代表第四代地面暗能量光谱仪（DESI）前两个月运行期间收集的发光红星系（LRG）数据的模拟星表，对两点相关函数（2PCF）的半解析、半经验协方差矩阵进行了扩展验证。我们在多个经相应截断处理的扩展泽利多维奇（EZ）模拟星系星表上运行该流程，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。我们提出对现有形式体系进行扩展，使其适用于采用标准重建算法处理的星表。我们详细考虑了协方差矩阵的比较方法，重点阐释了由样本方差导致的解释特征和统计特性，特别是当外部协方差估计完全准确时某些度量指标的期望值仍呈现非平凡性。通过改进的模拟星表与验证技术，我们确认了预测值与样本协方差之间的良好一致性。这使得在不需创建大量具有匹配成团性的模拟星系星表的情况下，仅需利用数据本身的2PCF测量即可生成可比数据集的协方差矩阵。本文使用的代码已在https://github.com/oliverphilcox/RascalC公开。