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：我们针对第四阶段地基暗能量光谱巡天（DESI）运行初期两个月采集的明亮红星系（LRG）代表性模拟星表，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。通过在多次扩展Zel'dovich（EZ）模拟星系星表上运行流水线并施加相应截断，我们将结果与模拟样本协方差进行对比，以评估其精度及波动性。我们提出了对先前开发形式主义的扩展，使其适用于经标准重构算法处理的星表。我们详细考察了协方差矩阵的比较方法，着重阐明其因样本方差引起的统计特性及物理解释——特别指出即使在外界协方差估计完美的情况下，某些度量指标的期望值仍存在非平凡特征。借助改进的模拟样本与验证技术，我们确认了预测结果与样本协方差之间具有良好一致性。这使得我们能够为同类数据集生成协方差矩阵，而无需创建众多具有匹配成团特征的模拟星系星表，仅需数据本身的2PCF测量值。本文代码已在https://github.com/oliverphilcox/RascalC 公开。