cosmology with galaxy clusters from the sdss maxbcg sample
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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
Cosmology with Galaxy Clusters from the SDSS maxBCG Sample

Jochen Weller

Annalisa Mana, TommasoGiannantonio,

GertHütsi

Recontres de Moriond 2012

theory counting halos in simulations

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
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
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
The Counts Data

Recontres de Moriond 2012

counts vs theory
Counts vs. Theory

Recontres de Moriond 2012

cosmology with number counts
Cosmology with Number Counts
  • Ωm = 0.282σ8 = 0.85
  • Ωm = 0.2
  • σ8 = 0.78

Recontres de Moriond 2012

scaling relation and scatter
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
Mass Data
  • stacked weak lensing
  • fit by fixing: M1 = 1.3×1014 M and M2 = 1.3×1015 Mand 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
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
The Power Spectrum of maxBCG Clusters

Hütsi 2009

Recontres de Moriond 2012

non linear corrections and photo z smoothing
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
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
Bias vs. Mass Selection

Recontres de Moriond 2012

model and priors
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
Power Spectrum Included

Recontres de Moriond 2012

parameter degeneracies
Parameter Degeneracies

Recontres de Moriond 2012

models vs data
Models vs. Data

Recontres de Moriond 2012

marginalized values
Marginalized Values

Recontres de Moriond 2012

summary
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
Outlook

maxBCG

eRosita

Euclid

Recontres de Moriond 2012

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