GALAXY CLUSTERING. Emmanuel Schaan AST 542 April 10th 2013

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1 GALAXY CLUSTERING Emmanuel Schaan AST 542 April 10th 2013

2 INTRODUCTION: SCALES

3 GALAXIES: 10kpc Milky Way: 10kpc, Mo

4 GALAXY GROUPS: 100kpc or «poor clusters» Local Group: ~50gal, 3Mpc, Mo

5 GALAXY CLUSTERS: 1Mpc or «rich clusters» Virgo Cluster: ~1000 gal, 2.2Mpc, Mo

GALAXY SUPERCLUSTERS: 10Mpc Superclusters: 10-100clusters,

6 GALAXY SUPERCLUSTERS: 10Mpc Superclusters: clusters, 10Mpc sheets, filaments, tracing the cosmic web of dark matter

7 GALAXY CLUSTERING What? Why? Statistical properties of the distribution of galaxies on various scales Tracers of DM cosmology! Clues on galaxy evolution

8 OUTLINE Methods to study galaxy clustering Cosmological contribution to the correlation function Clues on galaxy evolution

9 TOOLS AND METHODS

10 AVAILABLE SURVEYS 2DFGRS: 2, 220kgal, z<0.22 6DFGRS: 6, 130kgal, not as deep SDSS:1/4 sky, ~1Mgal, z<0.15, high resolution local survey DEEP2: 4 fields of 120 x30, 0.75<z<1.4, resolution comparable to SDSS COSMOS: 2sq deg, up to z~5, (photo-z) NMBS: up to z~3, (photo-z)

11 CORRELATION FUNCTION Usual tool to study noise Continuum definition: δ (x ) := (n(x ) n) /n ξ (r ) := δ (x ) δ (x +r ) n (x ) n (x +r ) = n 2 1+δ (x ) δ (x +r )

12 CORRELATION FUNCTION Interpretation as extra probability on top of uniform Poisson sampling. n (x ) n (x +r ) = n 2 1+δ (x ) δ (x +r ) P (x +r x ) =(nv ) 1+ξ (r ) r?

13 ESTIMATOR FOR CORRELATION FUNCTION Galaxy survey Mock map: Poisson sampling of a uniform galaxy number density from Coil et al. 2004

14 ESTIMATOR FOR CORRELATION FUNCTION Galaxy survey Mock map: Poisson sampling of a uniform galaxy number density Window function from Coil et al. 2004

15 ESTIMATOR FOR CORRELATION FUNCTION Galaxy survey Mock map: Poisson sampling of a uniform galaxy number density Window function Redshift selection function from Coil et al. 2004

16 ESTIMATOR FOR CORRELATION FUNCTION Galaxy survey Mock map: Poisson sampling of a uniform galaxy number density Window function Redshift selection function Discrete estimator (Landy & Szalay estimator 1993): ξ = DD SS ns n D 2 2 DS SS ns n D +1 from Coil et al. 2004

17 REDSHIFT-SPACE DISTORTIONS Small scales: finger of god Large scales: densityvelocity correlation SDSS data, Zehavi et al. 2010

18 GALAXY BIAS Untangling cosmology and galaxy evolution: δ g (m, color,...)=b g (m, color,...)δ matter n g = n g [1 + b g δ matter ] In practice: b g = σ g(color,...) σ matter

19 COSMOLOGICAL INSIGHTS ON/FROM THE GALAXY CORRELATION FUNCTION

20 GALAXY CORRELATION FUNCTION from Zehavi et al. 2002

21 LINK WITH MATTER P(k,z=0) [Mpc 3 ] SDSS DR7 (Reid et al. 2010) LyA (McDonald et al. 2006) ACT CMB Lensing (Das et al. 2011) ACT Clusters (Sehgal et al. 2011) CCCP II (Vikhlinin et al. 2009) BCG Weak lensing (Tinker et al. 2011) ACT+WMAP spectrum (this work) from Hlozek et al (ACT collaboration) k [Mpc 1 ] 10 2 linear scale cluster size 10 1

22 LINK WITH MATTER P(k,z=0) [Mpc 3 ] SDSS DR7 (Reid et al. 2010) LyA (McDonald et al. 2006) ACT CMB Lensing (Das et al. 2011) ACT Clusters (Sehgal et al. 2011) CCCP II (Vikhlinin et al. 2009) BCG Weak lensing (Tinker et al. 2011) ACT+WMAP spectrum (this work) from Hlozek et al (ACT collaboration) k [Mpc 1 ] 10 2 linear scale cluster size 10 1

23 HALO MODEL from Cooray & Sheth 2002 P gal (k)=pgal(k)+p 1h gal(k) 2h, P 1h gal(k)= P 2h gal (k) P lin (k) where dm n(m) N gal(n gal 1) m n 2 gal [ dm n(m) b 1 (m) N gal m n gal u gal (k m) p, u gal (k m) ] 2 distribution of galaxies inside halos (small scales) distribution of halos (large scales)

24 «COSMIC COINCIDENCE» 3 log(m min )=12.3 log!(r) z = -0.9 z = log!(r) 1 z = 1.0 z = r [Mpc h -1 ] from Watson et al r [Mpc h -1 ]

25 CLUES ON GALAXY EVOLUTION

26 z < 0.15 Red galaxies more clustered Bright galaxies more clustered from Zehavi et al. 2002, 2011

27 0.7 < z < 1.35 Red more clustered, b/b = 1.4 Brighter galaxies more clustered from Coil et al. 2004

28 Star forming galaxies at z~2 identify mass of halo hosts simulate halos from Conroy et al Conclusion: 50% survive, of which 30% become satellite galaxies

29 DISTANT RED GALAXIES AT 2< z <3 Incompatibility between number density and clustering Conclusion: incorrect biasing for DRGs? from Quadri et al. 2008

30 CONCLUSION Galaxy clustering cosmology Clues on galaxy evolution

31 REFERENCES Coil et al Conroy et al Quadri et al Zehavi et al. 2002, 2010, 2011 Hlozek et al Cooray Sheth 2002 Watson et al. 2011

32 LYMAN ALPHA EMITTERS Fig. 5. Constraints on age of the young stellar population vs. its mass fraction.

Clustering of galaxies

Clustering of galaxies Notions: peculiar velocities, redshift space Correlation function: definitions and methods of estimation Power Spectrum Effects: redshift distortions Biases and biasing Observations!