Where do Luminous Red Galaxies form?

The clustering of galaxies 2dF Galaxy Redshift Survey Redshift Distance (billions of light yrs) You are here

The clustering of galaxies... encodes cosmological / astrophysical information: The physical processes through which matter fluctuations grow in the Universe under gravity (including inflation) The underlying constituents of the Universe (the cosmological parameters) The formation processes of different types of galaxies (the environments in which they live)

The SDSS Luminous Red Galaxy photo-z sample Collaborators: Adrian Collister, Sarah Bridle, Ofer Lahav Also see: Nikhil Padmanabhan & SDSS team

The SDSS Luminous Red Galaxy photo-z sample Efficient probes of large-scale structure owing to their high clustering amplitude Excellent photo-z performance (dz ~ 0.04) owing to rapidly-changing colours with redshift SDSS imaging survey provides high-quality LRG catalogue, probing higher redshifts (0.4 < z < 0.7) than the SDSS spectroscopic LRG survey The largest cosmic volume mapped! (~ 7 Gpc 3 )

The SDSS Luminous Red Galaxy photo-z sample Edge of the Visible Universe Most Distant Object Known Luminous Red Galaxies Regular Galaxies Image credit: Nikhil Padmanabhan SDSS Telescope

The SDSS Luminous Red Galaxy photo-z sample Colour cuts select 1.1 million LRGs over 5900 sq deg

The SDSS Luminous Red Galaxy photo-z sample We have 13,000 spec-z s for these LRGs thanks to the 2dF-SDSS LRG and Quasar (2SLAQ) survey Determine photo-z s by neural network ( ANNz )

Clustering measurements (1) Large-scale clustering (> 1 deg) - angular power spectrum in redshift slices dz = 0.05 Measurement of cosmological parameters using power spectrum models in the quasi-linear regime

Angular power spectrum measurements

Clustering measurements (2) Small-scale clustering (< 1 deg) - angular correlation function in redshift slices dz = 0.05 Can we detect any halo model features?

Angular correlation function measurements

Modelling the correlation function

Modelling the correlation function N-body simulations tell us... The mass function of dark matter haloes The clustering properties of dark matter haloes The density profile of dark matter haloes The growth with time of dark matter haloes

Modelling the correlation function But let s adopt a simple halo model...! Number of galaxies N(M) Dark matter halo mass M

Modelling the correlation function But let s adopt a simple halo model...! Number of galaxies N(M) 1 0 Central galaxies Mcut Dark matter halo mass M

Modelling the correlation function But let s adopt a simple halo model...! Number of galaxies N(M) 1 0 Central galaxies Mcut, Mwidth Dark matter halo mass M

Modelling the correlation function But let s adopt a simple halo model...! Number of galaxies N(M) 1 0 Central galaxies Mcut, Mwidth (M/M0) b Satellite galaxies Dark matter halo mass M

Modelling the correlation function But let s adopt a simple halo model...! [3 params] Number of galaxies N(M) 1 0 Central galaxies Mcut, Mwidth (M/M0) b Satellite galaxies Dark matter halo mass M

Modelling the correlation function 1-halo pairs 2-halo pairs

Modelling the correlation function Best-fit power-law 1-halo pairs 2-halo pairs

Halo model measurements

Halo model measurements Increasing luminosity

Halo model measurements Effective mass of hosting haloes = 10 13.7-14.0 Msolar Average number of LRGs hosted by a halo of mass 10 14.5 Msolar = (2.7, 2.0, 1.3, 0.8) Fraction of satellite galaxies = 5% Power-law slope of halo occupation N(M) for satellite galaxies increases strongly with luminosity

To sum up... Modern galaxy surveys detect deviations from power-law correlation function These deviations allow us to measure how these galaxies populate dark matter haloes Observations are in impressive agreement with dark matter theory (N-body simulations)