Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for early DESI data

• Published in: Monthly notices of the Royal Astronomical Society Vol. 524; no. 3; pp. 3894 - 3911
• Main Authors: Rashkovetskyi, Michael, Eisenstein, Daniel J, Aguilar, Jessica Nicole, Brooks, David, Claybaugh, Todd, Cole, Shaun, Dawson, Kyle, de la Macorra, Axel, Doel, Peter, Fanning, Kevin, Font-Ribera, Andreu, Forero-Romero, Jaime E, Gontcho A Gontcho, Satya, Hahn, ChangHoon, Honscheid, Klaus, Kehoe, Robert, Kisner, Theodore, Landriau, Martin, Levi, Michael, Manera, Marc, Miquel, Ramon, Moon, Jeongin, Nadathur, Seshadri, Nie, Jundan, Poppett, Claire, Ross, Ashley J, Rossi, Graziano, Sanchez, Eusebio, Saulder, Christoph, Schubnell, Michael, Seo, Hee-Jong, Tarle, Gregory, Valcin, David, Weaver, Benjamin Alan, Zhao, Cheng, Zhou, Zhimin, Zou, Hu
• Format: Journal Article
• Language: English
• Published: United States Oxford University Press 24.07.2023
• Subjects: ASTRONOMY AND ASTROPHYSICS; cosmology; data analysis; galaxies; large-scale structure of the universe; software; statistical methods; surveys; theory; surveys; methods: statistical; software: data analysis; galaxies: statistics; large-scale structure of Universe; cosmology: theory
• ISSN: 0035-8711; 1365-2966
• DOI: 10.1093/mnras/stad2078

ABSTRACT 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 (LRGs) data collected during the initial 2 months of operations of the Stage-IV ground-based Dark Energy Spectroscopic Instrument (DESI). We run the pipeline on multiple effective 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, non-trivial 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 data sets 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.