Comparing approximate methods for mock catalogues and covariance matrices – I. Correlation function Article Swipe
Martha Lippich
,
Ariel G. Sánchez
,
Manuel Colavincenzo
,
E. Sefusatti
,
Pierluigi Monaco
,
L Blot
,
M. Crocce
,
Marcelo A. Alvarez
,
A. S. Agrawal
,
S. Àvila
,
A. Balaguera-Antolínez
,
Richard Bond
,
Sandrine Codis
,
Claudio Dalla Vecchia
,
Antonio D. Montero-Dorta
,
P. Fosalba
,
Albert Izard
,
Francisco-Shu Kitaura
,
Marcos Pellejero-Ibáñez
,
George Stein
,
Mohammadjavad Vakili
,
Gustavo Yepes
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/sty2757
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1093/mnras/sty2757
This paper is the first in a set that analyses the covariance matrices of clustering statistics obtained from several approximate methods for gravitational structure formation. We focus here on the covariance matrices of anisotropic two-point correlation function measurements. Our comparison includes seven approximate methods, which can be divided into three categories: predictive methods that follow the evolution of the linear density field deterministically (ICE-COLA, PEAK PATCH, and PINOCCHIO), methods that require a calibration with N-body simulations (PATCHY and HALOGEN), and simpler recipes based on assumptions regarding the shape of the probability distribution function (PDF) of density fluctuations (lognormal and Gaussian density fields). We analyse the impact of using covariance estimates obtained from these approximate methods on cosmological analyses of galaxy clustering measurements, using as a reference the covariances inferred from a set of full N-body simulations. We find that all approximate methods can accurately recover the mean parameter values inferred using the N-body covariances. The obtained parameter uncertainties typically agree with the corresponding N-body results within 5 per cent for our lower mass threshold and 10 per cent for our higher mass threshold. Furthermore, we find that the constraints for some methods can differ by up to 20 per cent depending on whether the halo samples used to define the covariance matrices are defined by matching the mass, number density, or clustering amplitude of the parent N-body samples. The results of our configuration-space analysis indicate that most approximate methods provide similar results, with no single method clearly outperforming the others.
Related Topics
Concepts
Covariance
Physics
Cluster analysis
Covariance matrix
Covariance function
Gaussian
Halo
Correlation function (quantum field theory)
Probability density function
Function (biology)
Galaxy
Statistical physics
Statistics
Astrophysics
Mathematics
Quantum mechanics
Dielectric
Biology
Evolutionary biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/mnras/sty2757
- https://academic.oup.com/mnras/article-pdf/482/2/1786/26370562/sty2757.pdf
- OA Status
- bronze
- Cited By
- 79
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2810826677
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2810826677Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/mnras/sty2757Digital Object Identifier
- Title
-
Comparing approximate methods for mock catalogues and covariance matrices – I. Correlation functionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-10-15Full publication date if available
- Authors
-
Martha Lippich, Ariel G. Sánchez, Manuel Colavincenzo, E. Sefusatti, Pierluigi Monaco, L Blot, M. Crocce, Marcelo A. Alvarez, A. S. Agrawal, S. Àvila, A. Balaguera-Antolínez, Richard Bond, Sandrine Codis, Claudio Dalla Vecchia, Antonio D. Montero-Dorta, P. Fosalba, Albert Izard, Francisco-Shu Kitaura, Marcos Pellejero-Ibáñez, George Stein, Mohammadjavad Vakili, Gustavo YepesList of authors in order
- Landing page
-
https://doi.org/10.1093/mnras/sty2757Publisher landing page
- PDF URL
-
https://academic.oup.com/mnras/article-pdf/482/2/1786/26370562/sty2757.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://academic.oup.com/mnras/article-pdf/482/2/1786/26370562/sty2757.pdfDirect OA link when available
- Concepts
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Covariance, Physics, Cluster analysis, Covariance matrix, Covariance function, Gaussian, Halo, Correlation function (quantum field theory), Probability density function, Function (biology), Galaxy, Statistical physics, Statistics, Astrophysics, Mathematics, Quantum mechanics, Dielectric, Biology, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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79Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 7, 2023: 10, 2022: 11, 2021: 20Per-year citation counts (last 5 years)
- References (count)
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63Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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