학술논문

Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data
Document Type
Working Paper
Source
Subject
Astrophysics - Cosmology and Nongalactic Astrophysics
Physics - Data Analysis, Statistics and Probability
Language
Abstract
We present an optimized way of producing the fast semi-analytical covariance matrices for the Legendre moments of the two-point correlation function, taking into account survey geometry and mimicking the non-Gaussian effects. We validate the approach on simulated (mock) catalogs for different galaxy types, representative of the Dark Energy Spectroscopic Instrument (DESI) Data Release 1, used in 2024 analyses. We find only a few percent differences between the mock sample covariance matrix and our results, which can be expected given the approximate nature of the mocks, although we do identify discrepancies between the shot-noise properties of the DESI fiber assignment algorithm and the faster approximation used in the mocks. Importantly, we find a close agreement (<~ 5% relative differences) in the projected errorbars for distance scale parameters for the baryon acoustic oscillation measurements. This confirms our method as an attractive alternative to simulation-based covariance matrices, especially for non-standard models or galaxy sample selections, in particular, relevant to the broad current and future analyses of DESI data.
Comment: Supporting publication of DESI 2024 III: Baryon Acoustic Oscillations from Galaxies and Quasars (arXiv:2404.03000). 29 pages, 4 figures. Prepared for submission to JCAP. Code available at https://github.com/oliverphilcox/RascalC and https://github.com/misharash/RascalC-scripts/tree/DESI2024. Data points from the plots available at https://zenodo.org/doi/10.5281/zenodo.10895161