The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics

The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of analysis including cosmological measurements, modelling, covariance and parameter likelihood. In this paper we present the results of an end-to-end simulation study designed to test the analysis pipeline for the combination of the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset and separate weak gravitational lensing information from the Kilo-Degree Survey, Dark Energy Survey and Hyper-Suprime-Cam Survey. Our analysis employs the 3x2-pt correlation functions including cosmic shear and galaxy-galaxy lensing, together with the projected correlation function of the spectroscopic DESI lenses. We build realistic simulations of these datasets including galaxy halo occupation distributions, photometric redshift errors, weights, multiplicative shear calibration biases and magnification. We calculate the analytical covariance of these correlation functions including the Gaussian, noise and super-sample contributions, and show that our covariance determination agrees with estimates based on the ensemble of simulations. We use a Bayesian inference platform to demonstrate that we can recover the fiducial cosmological parameters of the simulation within the statistical error margin of the experiment, investigating the sensitivity to scale cuts. This study is the first in a sequence of papers in which we present and validate the large-scale 3x2-pt cosmological analysis of DESI-Y1.

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Journal
The Open Journal of Astrophysics
Published
2025-03-07
DOI
https://doi.org/10.33232/001c.131903
Primary Topic
Statistical and numerical algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics

S. Heydenreich, J. Lange, C. García-Quintero, D. Schlegel et al.
The Open Journal of Astrophysics
Statistical and numerical algorithms
article

The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics

S. Heydenreich, J. Lange, C. García-Quintero, D. Schlegel, Diane Kirkby, G. Niz, J. DeRose, N Emas, A. Porredon, Shahab Joudaki, Ignasi Pérez-Ràfols, Axel de la Macorra, B. A. Weaver, Alex Krolewski, Simone Ferraro, Zechang Sun, S. Ahlen, M. E. Levi, Rossana Ruggeri, Eric Jullo, M. Landriau, Marc Manera, E. Sanchez, G. Tarlé, K. Honscheid, Cullan Howlett, Davide Bianchi, W. J. Percival, A. Leauthaud, David H. Brooks, A. Dey, G. Gutiérrez, Cullen H. Blake, John Moustakas, R Miquel, P. Doel, J. E. Forero-Romero, C. Saulder, M Ishak, T. Claybaugh, K. Fanning, R. Kehoe, D. Sprayberry
article en

Abstract

The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of analysis including cosmological measurements, modelling, covariance and parameter likelihood. In this paper we present the results of an end-to-end simulation study designed to test the analysis pipeline for the combination of the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset and separate weak gravitational lensing information from the Kilo-Degree Survey, Dark Energy Survey and Hyper-Suprime-Cam Survey. Our analysis employs the 3x2-pt correlation functions including cosmic shear and galaxy-galaxy lensing, together with the projected correlation function of the spectroscopic DESI lenses. We build realistic simulations of these datasets including galaxy halo occupation distributions, photometric redshift errors, weights, multiplicative shear calibration biases and magnification. We calculate the analytical covariance of these correlation functions including the Gaussian, noise and super-sample contributions, and show that our covariance determination agrees with estimates based on the ensemble of simulations. We use a Bayesian inference platform to demonstrate that we can recover the fiducial cosmological parameters of the simulation within the statistical error margin of the experiment, investigating the sensitivity to scale cuts. This study is the first in a sequence of papers in which we present and validate the large-scale 3x2-pt cosmological analysis of DESI-Y1.

The Open Journal of AstrophysicsVol. 8
Boston University (US), Institució Catalana de Recerca i Estudis Avançats (ES), Universitat Autònoma de Barcelona (ES), Universidad de Guanajuato (MX), Fermi National Accelerator Laboratory (US), Hiroshima Shudo University (JP), Berkeley College (US), Harvard University (US), High Energy Accelerator Research Organization (JP), University of Waterloo (CA), The University of Queensland (AU), Southern Methodist University (US), Perimeter Institute (CA), University of California, Santa Cruz (US), Institute for High Energy Physics (ES), University of Milan (IT), Regional Municipality of Waterloo (CA), Sejong University (KR), National Solar Observatory (US), Laboratoire d’Astrophysique de Marseille (FR), Institut Català de Ciències del Clima (ES), Lickenbrock (US), Michigan United (US), Unidades Centrales Científico-Técnicas (ES), UC Irvine Health (US), Mylan (Switzerland) (CH), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), State Library of Ohio (US), University College London (GB), Swinburne University of Technology (AU), Universidad Intercontinental (MX), Universidad Nacional Autónoma de México (MX), Siena College (US), Netherlands Institute for Radio Astronomy (NL), Universitat Politècnica de Catalunya (ES), Tsinghua University (CN)
National Science Foundation, U.S. Department of Energy, National Aeronautics and Space Administration, Gordon and Betty Moore Foundation, Space Telescope Science Institute, Ministerio de Ciencia, Innovación y Universidades, Commissariat à l'Énergie Atomique et aux Énergies Alternatives, Office of Science, Science and Technology Facilities Council, Agencia Estatal de Investigación, Division of Astronomical Sciences
Openalex Percentile: Top 93%
Statistical and numerical algorithms
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