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Combining cosmological constraints from cluster counts and galaxy clustering

Published online by Cambridge University Press:  01 July 2015

F. Lacasa
F. Lacasa*
Affiliation:
ICTP South American Institute for Research & Instituto de Física Teórica - UNESP, Rua Dr. Bento Teobaldo Ferraz 271, Bloco 2 - Barra Funda, 01140-070 São Paulo, SP, Brazil email: fabien@ift.unesp.br
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Abstract

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Present and future large scale surveys offer promising probes of cosmology. For example the Dark Energy Survey (DES) is forecast to detect ~300 millions galaxies and thousands clusters up to redshift ~1.3. I here show ongoing work to combine two probes of large scale structure : cluster number counts and galaxy 2-point function (in real or harmonic space). The halo model (coupled to a Halo Occupation Distribution) can be used to model the cross-covariance between these probes, and I introduce a diagrammatic method to compute easily the different terms involved. Furthermore, I compute the joint non-Gaussian likelihood, using the Gram-Charlier series. Then I show how to extend the methods of Bayesian hyperparameters to Poissonian distributions, in a first step to include them in this joint likelihood.

Keywords

galaxies: statisticsmethods: data analysiscosmologyjoint probes
Type
Contributed Papers
Information
Proceedings of the International Astronomical Union , Volume 10 , Symposium S306: Statistical Challenges in 21st Century Cosmology , May 2014 , pp. 216 - 218
Copyright
Copyright © International Astronomical Union 2015 

References

Planck Collaboration (2014), “Planck 2013 results. XX. Cosmology from Sunyaev-Zeldovich cluster counts”, arXiv:1303.5080Google Scholar
Lacasa, F. & Rosenfeld, R., “Combining cluster counts and galaxy clustering cosmological constraints”, in prep.Google Scholar
Lacasa, F.et al. (2014), “Non-Gaussianity of the CIB anisotropies - I. Diagrammatic formalism and application to the angular bispectrum”, MNRAS, Vol 439, p.123–142CrossRefGoogle Scholar
Hobson, M. P.et al., “Combining cosmological data sets: hyperparameters and Bayesian evidence”, MNRAS, Vol 335, pp. 377–388Google Scholar