Analytical and EZmock covariance validation for the DESI 2024 results

Abstract The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full-Shape two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on an ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full-Shape analyses, which motivates the use of a corrected mock covariance for DESI's 2024 Full Shape analysis.

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Publication Details

Journal
Journal of Cosmology and Astroparticle Physics
Published
2025-04-01
DOI
https://doi.org/10.1088/1475-7516/2025/04/055
Citations
15
Primary Topic
Particle Detector Development and Performance
Type
article
Field-Weighted Citation Impact
28.05

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article

Analytical and EZmock covariance validation for the DESI 2024 results

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Journal of Cosmology and Astroparticle Physics
Particle Detector Development and Performance
28.05
article

Analytical and EZmock covariance validation for the DESI 2024 results

C. García-Quintero, E. Chaussidon, Michael S. Schubnell, N. Palanque‐Delabrouille, S. Nadathur, Gregory Tarlé, Laurent Le Guillou, D. Bianchi, David J. Schlegel, Ignasi Pérez-Ràfols, Axel de la Macorra, Héctor Gil-Marín, O. Alves, Martin Landriau, M. Enriquez-Vargas, David J. Brooks, Gaston Gutierrez, Eusebio Sanchez, J. Mena-Fernández, ENRIQUE GAZTAÑAGA, A. de Mattia, Cullan Howlett, Steven P. Ahlen, Pauline Zarrouk, R. Miquel, Theodore S. Kisner, M. Rashkovetskyi, John Moustakas, Z. Ding, D. Forero-Sánchez, U. Andrade, Xinyi Chen, Hee‐Jong Seo, Jiaxi Yu, M. Vargas-Magaña, Hu Zou, Graziano Rossi, S. Cole, Klaus Honscheid, T. Claybaugh, E. Burtin, D. Sprayberry
article en
15 citations

Abstract

Abstract The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full-Shape two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on an ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full-Shape analyses, which motivates the use of a corrected mock covariance for DESI's 2024 Full Shape analysis.

Journal of Cosmology and Astroparticle PhysicsVol. 2025(04)
Boston University (US), Institució Catalana de Recerca i Estudis Avançats (ES), Fermi National Accelerator Laboratory (US), Hiroshima Shudo University (JP), Harvard University (US), High Energy Accelerator Research Organization (JP), University of Waterloo (CA), The University of Queensland (AU), Shanghai Jiao Tong University (CN), Perimeter Institute (CA), Institute for High Energy Physics (ES), University of Milan (IT), University of Michigan (US), Regional Municipality of Waterloo (CA), Sejong University (KR), National Solar Observatory (US), Institut d'Estudis Espacials de Catalunya (ES), Institut Català de Ciències del Clima (ES), Laboratoire de Physique Nucléaire et de Hautes Énergies (FR), Indiana Cancer Consortium (US), Michigan United (US), Unidades Centrales Científico-Técnicas (ES), Institut Cartogràfic i Geològic de Catalunya (ES), Laboratoire de Physique Subatomique et de Cosmologie (FR), Mylan (Switzerland) (CH), Las Campanas Observatory (CL), National Astronomical Observatories (CN), Institut de Recherche sur les Lois Fondamentales de l'Univers (FR), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), State Library of Ohio (US), University College London (GB), École Polytechnique Fédérale de Lausanne (CH), University of Portsmouth (GB), Universidad Nacional Autónoma de México (MX), Siena College (US), Netherlands Institute for Radio Astronomy (NL), Universitat Politècnica de Catalunya (ES)
National Science Foundation, U.S. Department of Energy, Gordon and Betty Moore Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Consejo Nacional de Ciencia y Tecnología, Ministerio de Ciencia e Innovación, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Office of Science, Science and Technology Facilities Council, Division of Astronomical Sciences, High Energy Physics
Openalex Percentile: Top 0%
Particle Detector Development and Performance
28.05
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