Cosmology and Galaxy Evolution from Galaxy Clustering Zheng

Cosmology and Galaxy Evolution from Galaxy Clustering Zheng Institute for Advanced Study

Outline: q Halo Occupation Distribution (HOD) q Breaking the Degeneracies between Cosmology and Galaxy Bias With David Weinberg (Ohio State University) (Zheng & Weinberg, astro-ph/0512071) q Evolution of Galaxies from HOD Modeling of DEEP 2 and SDSS Galaxy Clustering With Alison Coil (University of Arizona) Idit Zehavi (Case Western Reserve University)

Snapshot @ z~1100 Light-Mass relation well understood CMB from WMAP Snapshot @ z~0 Light-Mass relation not well understood Galaxies from SDSS

Cosmological Model initial conditions energy & matter contents Galaxy Formation Physics gas dynamics, cooling star formation, feedback m 8 ns Dark Halo Population n(M) (r|M) v(r|M) Halo Occupation Distribution P(N|M) spatial bias within halos velocity bias within halos Galaxy Clustering Galaxy-Mass Correlations

Halo Occupation Distribution (HOD) • P(N|M) Probability distribution of finding N galaxies in a halo of virial mass M mean occupation <N(M)> + higher moments • Spatial bias within halos Difference in the distribution profiles of dark matter and galaxies within halos • Velocity bias within halos Difference in the velocities of dark matter and galaxies within halos e. g. , Jing & Borner 1998; Ma & Fry 2000; Peacock & Smith 2000; Seljak 2000; Scoccimarro et al. 2001; Berlind & Weinberg 2002; Yang, Mo, & van den Bosch 2003; …

Cosmological Model initial conditions energy & matter contents Galaxy Formation Physics gas dynamics, cooling star formation, feedback m 8 ns Dark Halo Population n(M) (r|M) v(r|M) Halo Occupation Distribution P(N|M) spatial bias within halos velocity bias within halos Galaxy Clustering Galaxy-Mass Correlations

Cosmological Model initial conditions energy & matter contents Galaxy Formation Physics gas dynamics, cooling star formation, feedback m 8 ns Dark Halo Population n(M) (r|M) v(r|M) Halo Occupation Distribution P(N|M) spatial bias within halos velocity bias within halos Galaxy Clustering Galaxy-Mass Correlations

Cosmology from Galaxy Clustering? Cosmology A HOD A Halo Population A Galaxy Clustering Galaxy-Mass Correlations A Cosmology B Halo Population B = HOD B Galaxy Clustering Galaxy-Mass Correlations B

Halo populations from distinct cosmological models Changing m with 8, ns, and Fixed Zheng, Tinker, Weinberg, & Berlind 2002

Cosmology A HOD A Halo Population A Galaxy Clustering Galaxy-Mass Correlations A = Cosmology B Halo Population B HOD B Galaxy Clustering Galaxy-Mass Correlations B

Flexible HOD parameterization • P(N|M) Motivated by galaxy formation models Kravtsov et al. 2004; Zheng et al. 2005 • Spatial bias within halos Different concentrations of galaxy distribution and dark matter distribution ( c) • Velocity bias within halos v g= v v m

Observational quantities • Spatial Clustering Galaxy overdensity g(r) Group multiplicity function ngroup(>N) 2 -point and 3 -point correlation function of galaxies • Dynamically Sensitive Observables m 0. 6/bg Pairwise velocity dispersion v(r) Average virial mass of galaxy groups <Mvir(N)> • Galaxy-mass cross-correlation function m gm(r)

Constraints on HOD and cosmological parameters Changing m with 8, ns, and Fixed

Constraints on HOD parameters Changing m with 8, ns, and Fixed

Constraints on cosmological parameters Changing m only Cluster-normalized Changing 8 only Halo MF matched

Influence Matrix

Constraints on cosmological parameters Forecast : ~10% on m ~10% on 8 Abazajian et al. 2005 ~5% on 8 m 0. 75 From 30 observables of 8 different statistics with 10% fractional errors

Conclusion Galaxy bias and cosmology are not degenerate with respect to galaxy clustering. * HOD modeling can greatly increase the cosmological power of galaxy redshift survey by taking the advantage of high-precision clustering measurements at small and intermediate scales. * Simultaneously, using galaxy clustering data, we can constrain the HODs for different classes of galaxies, which provide valuable tests of galaxy formation models.

Cosmological Model initial conditions energy & matter contents Galaxy Formation Physics gas dynamics, cooling star formation, feedback m 8 ns Dark Halo Population n(M) (r|M) v(r|M) Halo Occupation Distribution P(N|M) spatial bias within halos velocity bias within halos Galaxy Clustering Galaxy-Mass Correlations

Galaxy Evolution from Galaxy Clustering Galaxy Samples Ø DEEP 2, z~1 (Coil et al 2006) Ø SDSS, z~0 (Zehavi et al 2005) Ø Measurements of two-point correlation functions as a function of luminosity

Two-point correlation function of galaxies Excess probability w. r. t. random distribution of finding galaxy pairs at a given separation 1 -halo term Central 2 -halo term Satellite

Halo Occupation Distribution For a sample of galaxies more luminous than Lmin Scatter between galaxy luminosity and host halo mass M 1 - mass of halos on average hosting one satellite galaxy above Lmin Mmin - characteristic minimum mass of halos hosting Lmin galaxies

Modeling results DEEP 2 galaxies L

Distribution of central galaxy luminosity

Mass scales of host halos

Establishing an evolution link between DEEP 2 and SDSS galaxies

Stellar mass evolution z~1 Star Merging Formation Merging z~0

Star formation efficiency vs Halo mass

Stellar mass evolution (z~1 to z~0) as a function of halo mass

z~1 Star Merging Formation z~0

Tentative conclusion: For central galaxies in z~0 M<1012 h-1 Msun halos, ~80% of their stars form after z~1 For central galaxies in z~0 M>1012 h-1 Msun halos, ~20 -40% of their stars form after z~1 Fardal et al. 2006

Summary & Future Work q HODs at z~1 and z~0 from modeling two-point correlation functions of DEEP 2 and SDSS galaxies Evolution link through halo evolution Stellar mass evolution from z~1 to z~0 for central galaxies as a function of halo mass (pure merger vs star formation) Useful constraints to galaxy formation models q Clustering measurements for galaxy samples based on stellar mass Galaxy samples at different redshifts Evolution of satellite galaxies