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Cosmology with Galaxy Clusters from the SDSS maxBCG Sample. Jochen Weller Annalisa Mana , Tommaso Giannantonio , Gert Hütsi. more low redshift clusters. more low mass clusters. Theory: Counting Halos in Simulations . Count halos in N-body simulations
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Cosmology with Galaxy Clusters from the SDSS maxBCG Sample Jochen Weller Annalisa Mana, TommasoGiannantonio, GertHütsi Recontres de Moriond 2012
more low redshift clusters more low mass clusters Theory: Counting Halos in Simulations • Count halos in N-body simulations • Measure “universal” mass function - density of cold dark matter halos of given mass Recontres de Moriond 2012
Universality of the Mass Function • Claims of universal parameterization in terms of linear fluctuation σ(M) • Tinker et al. 2008 find additional redshift dependence (strongest effect in amplitude, but also shape) • This effect can be included in parameterization Recontres de Moriond 2012
The SDSS maxBCG Sample • #13,823 • 7,500 deg2 • z=0.1-0.3 • red sequencemethod Catalogue: Koester et. al 2007 Cosmology: Rozo et al. 2009 Recontres de Moriond 2012
The Counts Data Recontres de Moriond 2012
Counts vs. Theory Recontres de Moriond 2012
Cosmology with Number Counts • Ωm = 0.282σ8 = 0.85 • Ωm = 0.2 • σ8 = 0.78 Recontres de Moriond 2012
Scaling Relation and Scatter • Assume linear scaling in log mass-richness relations: ln M = a lnNgal +b • Scatter constrained by x-ray and weak lensing data (Rozo et al. 2009) • For analysis we require: σNgal|lnM • Simply related via scaling relation: use as prior in analysis; related via slope Recontres de Moriond 2012
Mass Data • stacked weak lensing • fit by fixing: M1 = 1.3×1014 M and M2 = 1.3×1015 Mand ln N1 and ln N2 as free parameters • allow for bias in mass measurement by a factor β Johnston et al. 2007 Sheldon et al. 2007 Recontres de Moriond 2012
Results – Counts and Weak Lensing Mass Implemented into COSMOMC: Lewis & Bridle Consistent with Rozo et al. 2009 self calibraition: Majumdar & Mohr 2003 Lima & Hu 2005 Recontres de Moriond 2012
The Power Spectrum of maxBCG Clusters Hütsi 2009 Recontres de Moriond 2012
Non-linear Corrections and Photo-z Smoothing • qNL= 14: non-linear • σz= 59: photo-z smoothing • beff= 3.2: bias Hütsi 2009 Recontres de Moriond 2012
Bias for Clusters • Calculate from mass function via peak-background split (Tinker et al. 2010) • average bias Recontres de Moriond 2012 beff = Bb_i
Bias vs. Mass Selection Recontres de Moriond 2012
Model and Priors • nS =0.96 • h = 0.7 • Ωb = 0.045 • flat, ΛCDM • photo-z errors: σzphot|z= 0.008 • β=1.0±0.06 • σlnM see previous slide • B=1.0±0.15 • σz=30±10 • purity/completeness: Error added in quadrature: 5% Recontres de Moriond 2012
Power Spectrum Included Recontres de Moriond 2012
Parameter Degeneracies Recontres de Moriond 2012
Models vs. Data Recontres de Moriond 2012
Marginalized Values Recontres de Moriond 2012
Summary • Clusters selected with richness and weak lensing masses give meaningful cosmological constraints • crucial to understand nuisance parameters • power spectrum tightens constraints; but non-linear modelling required • more to come … different cosmologies, additional datasets Recontres de Moriond 2012
Outlook maxBCG eRosita Euclid Recontres de Moriond 2012
