Semi-analytical covariance matrices for two-point correlation function for DESI 2024 data
M. Rashkovetskyi, D. Forero-Sánchez, Arnaud de Mattia, Daniel J. Eisenstein, Nikhil Padmanabhan, Hee‐Jong Seo, Ashley J. Ross, J. Aguilar, S. P. Ahlen, O. Alves, U. Andrade, David J. Brooks, E. Burtin, T. Claybaugh, Shaun Cole, Axel de la Macorra, Z. Ding, P. Doel, K. Fanning, Simone Ferraro, Andreu Font-Ribera, J. E. Forero-Romero, C. García-Quintero, Héctor Gil-Marín, Satya Gontcho A Gontcho, A. X. Gonzalez-Morales, G. Gutiérrez, K. Honscheid, Cullan Howlett, S. Juneau, S. W. Allen, L. Le Guillou, Marc Manera, L. Medina-Varela, J. Mena-Fernández, R. Miquel, Eva-Maria Mueller, A. Muñoz-Gutiérrez, Adam D. Myers, Jundan Nie, Gustavo Niz, E. Paillas, Will J. Percival, Claire Poppett, Ignasi Pérez-Ràfols, Mehdi Rezaie, A.J Rosado-Marín, Graziano Rossi, Rossana Ruggeri, E. Sánchez, C. Saulder, David J. Schlegel, M. Schubnell, David Sprayberry, G. Tarlé, B. A. Weaver, Jiaxi Yu, Cheng Zhao, Hu Zou, H. Zou
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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 (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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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.
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27
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102
References
Tasks
Covariance, Covariance matrix, Correlation function (quantum field theory), Covariance function, Dark energy, Gaussian, Galaxy, Estimation of covariance matrices
Methods
Algorithm
Domains
Physics, Statistical physics, Physics and Astronomy, Astronomy and Astrophysics
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