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The two-point correlation function covariance with fewer mocks

delete2023-11-29
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OA
AI
S
S. Trusov *
P
Pauline Zarrouk
S
Shaun Cole
P
P. Norberg
C
Cheng Zhao
J
J. Aguilar
S
S. P. Ahlen
D
David J. Brooks
A
Axel de la Macorra
P
Peter Doel
A
Andreu Font-Ribera
K
K. Honscheid
T
Theodore Kisner
M
Martin Landriau
R
R. Miquel
J
Jundan Nie
C
Claire Poppett
M
Michael Schubnell
G
G. Tarlé
Z
Zhimin Zhou
DOI:10.1093/mnras/stad3710delete
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摘要

摘要

En 中文
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
Keyword:
dark energy
dark matter
large-scale structure of Universe
miscellaneous

期刊

Monthly Notices of the Royal Astronomical Society 封面图
Monthly Notices of the Royal Astronomical Society
IF:
4.8
论文数:
7.0W
被引数:
25.0W

机构

U
Universidad Nacional Autonoma de Mexico
学者数:
3.8W
论文数: 2.6W
被引数: 28
U
University System of Ohio
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15.4W
论文数: 13.0W
被引数: 200
B
barcelona institute of science & technology
学者数:
1.2W
论文数: 9.7K
被引数: 36
B
boston university
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论文数: 3.2W
被引数: 67
D
Durham University
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1.3W
论文数: 1.5W
被引数: 2.1W
E
Ecole Polytechnique Federale de Lausanne
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1.7W
论文数: 1.3W
被引数: 25
L
Lawrence Berkeley National Laboratory
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1.5W
论文数: 1.1W
被引数: 6.1W
U
Universite Paris Cite
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论文数: 6.3W
被引数: 604
U
united states department of energy (doe)
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11.3W
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被引数: 246
S
swiss federal institutes of technology domain
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9.0W
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I
institute for high energy physics (ifae)
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1.4K
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被引数: 13
S
Sorbonne Universite
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6.2W
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