The 2-point correlation function covariance with fewer mocks
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| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| author | Trusov, Svyatoslav Zarrouk, Pauline Cole, Shaun Norberg, Peder Zhao, Cheng Aguilar, Jessica Nicole Ahlen, Steven Brooks, David de la Macorra, Axel Doel, Peder Font-Ribera, Andreu Honscheid, Klaus Kisner, Theodore Landriau, Martin Magneville, Christophe Miquel, Ramon Nie, Jundan Poppett, Claire Schubnell, Michael Tarlé, Gregory Zhou, Zhimin |
| author_facet | Trusov, Svyatoslav Zarrouk, Pauline Cole, Shaun Norberg, Peder Zhao, Cheng Aguilar, Jessica Nicole Ahlen, Steven Brooks, David de la Macorra, Axel Doel, Peder Font-Ribera, Andreu Honscheid, Klaus Kisner, Theodore Landriau, Martin Magneville, Christophe Miquel, Ramon Nie, Jundan Poppett, Claire Schubnell, Michael Tarlé, Gregory Zhou, Zhimin |
| contents | We present an approach for accurate estimation of the covariance of 2-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 DESI LRG and ELG samples in the redshift range of z = [0.8, 1.2].
We find that the Mohammad-Percival correction produces a bias in the 2-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 find that our fitted jackknife approach based on 25 mocks is able to 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 2-point correlation function is therefore reduced by a factor of 40-60. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_16332 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The 2-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, Peder Font-Ribera, Andreu Honscheid, Klaus Kisner, Theodore Landriau, Martin Magneville, Christophe Miquel, Ramon Nie, Jundan Poppett, Claire Schubnell, Michael Tarlé, Gregory Zhou, Zhimin Cosmology and Nongalactic Astrophysics We present an approach for accurate estimation of the covariance of 2-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 DESI LRG and ELG samples in the redshift range of z = [0.8, 1.2]. We find that the Mohammad-Percival correction produces a bias in the 2-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 find that our fitted jackknife approach based on 25 mocks is able to 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 2-point correlation function is therefore reduced by a factor of 40-60. |
| title | The 2-point correlation function covariance with fewer mocks |
| topic | Cosmology and Nongalactic Astrophysics |
| url | https://arxiv.org/abs/2306.16332 |