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Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data

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arxiv 2404.03007 v5 pith:OIQ7QG4S submitted 2024-04-03 astro-ph.CO physics.data-an

classification astro-ph.COphysics.data-an
keywords covariancedesidatamatricesanalysescorrelationdifferencesfind
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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 (emulator) used in the mocks. Importantly, we find a close agreement (<=8% 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, making it particularly relevant to the broad current and future analyses of DESI data.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  5. Fiducial-Cosmology-dependent systematics for the DESI 2024 Full-Shape Analysis

    astro-ph.CO 2025-09 conditional novelty 6.0 of 10

    Changing the assumed fiducial cosmology in DESI DR1 full-shape mock analyses shifts inferred parameters by at most 0.22 sigma in full-modeling and 0.45 sigma in ShapeFit, both within the survey's statistical uncertainty.

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    astro-ph.CO 2025-08 conditional novelty 6.0 of 10

    DESI's first parity-violation search shows a strong auto-correlation signal that disappears when the sky is split into patches, pointing to underestimated mocks' variance rather than new physics.

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