Svyatoslav Trusov
The two-point correlation function covariance with fewer mocks
Trusov, Svyatoslav; Zarrouk, Pauline; Cole, Shaun; Norberg, Peder; Zhao, Cheng; Aguilar, Jessica Nicole; Ahlen, Steven; Brooks, David; de la Macorra, Axel; Doel, Peter; Font-Ribera, Andreu; Honscheid, Klaus; Kisner, Theodore; Landriau, Martin; Magneville, Christophe; Miquel, Ramon; Nie, Jundan; Poppett, Claire; Schubnell, Michael; Tarlé, Gregory; Zhou, Zhimin
Authors
Pauline Zarrouk
Professor Shaun Cole shaun.cole@durham.ac.uk
Director of the Institute for Computational Cosmology
Professor Peder Norberg peder.norberg@durham.ac.uk
Professor
Cheng Zhao
Jessica Nicole Aguilar
Steven Ahlen
David Brooks
Axel de la Macorra
Peter Doel
Andreu Font-Ribera
Klaus Honscheid
Theodore Kisner
Martin Landriau
Christophe Magneville
Ramon Miquel
Jundan Nie
Claire Poppett
Michael Schubnell
Gregory Tarlé
Zhimin Zhou
Abstract
We present FITCOV an approach for accurate estimation of the covariance of two-point correlation functions that requires fewer mocks than the standard mock-based covariance. This can be achieved by dividing a set of mocks into jackknife regions and fitting the correction term first introduced in Mohammad & Percival (2022), such that the mean of the jackknife covariances corresponds to the one from the mocks. This extends the model beyond the shot-noise limited regime, allowing it to be used for denser samples of galaxies. We test the performance of our fitted jackknife approach, both in terms of accuracy and precision, using lognormal mocks with varying densities and approximate EZmocks mimicking the Dark Energy Spectroscopic Instrument LRG and ELG samples in the redshift range of z = [0.8, 1.1]. We find that the Mohammad–Percival correction produces a bias in the two-point correlation function covariance matrix that grows with number density and that our fitted jackknife approach does not. We also study the effect of the covariance on the uncertainty of cosmological parameters by performing a full-shape analysis. We demonstrate that our fitted jackknife approach based on 25 mocks can recover unbiased and as precise cosmological parameters as the ones obtained from a covariance matrix based on 1000 or 1500 mocks, while the Mohammad–Percival correction produces uncertainties that are twice as large. The number of mocks required to obtain an accurate estimation of the covariance for the two-point correlation function is therefore reduced by a factor of 40–60. The FITCOV code that accompanies this paper is available at this GitHub repository.
Citation
Trusov, S., Zarrouk, P., Cole, S., Norberg, P., Zhao, C., Aguilar, J. N., …Zhou, Z. (2024). The two-point correlation function covariance with fewer mocks. Monthly Notices of the Royal Astronomical Society, 527(3), 9048-9060. https://doi.org/10.1093/mnras/stad3710
Journal Article Type | Article |
---|---|
Acceptance Date | Nov 28, 2023 |
Online Publication Date | Nov 29, 2023 |
Publication Date | 2024-01 |
Deposit Date | Mar 26, 2024 |
Publicly Available Date | Mar 26, 2024 |
Journal | Monthly Notices of the Royal Astronomical Society |
Print ISSN | 0035-8711 |
Electronic ISSN | 1365-2966 |
Publisher | Royal Astronomical Society |
Peer Reviewed | Peer Reviewed |
Volume | 527 |
Issue | 3 |
Pages | 9048-9060 |
DOI | https://doi.org/10.1093/mnras/stad3710 |
Public URL | https://durham-repository.worktribe.com/output/2348302 |
Files
Published Journal Article
(3.2 Mb)
PDF
Licence
http://creativecommons.org/licenses/by/4.0/
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
You might also like
The DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation
(2023)
Journal Article
A sparse regression approach for populating dark matter haloes and subhaloes with galaxies
(2022)
Journal Article
Solving small-scale clustering problems in approximate light-cone mocks
(2022)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search