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Clusters at low redshift Michael Balogh University of Durham University of Durham University of Waterloo (Canada) Collaborators Richard Bower Durham Ivan Baldry & Karl Glazebrook Johns Hopkins Bob Nichol, Chris Miller & Alex Gray Carnegie Mellon Vince Eke Durham GALFORM people: Baugh, Cole, Lacey, Frenk Durham Ian Lewis (Oxford) and the 2dFGRS team Outline 1. Background: Galaxy properties as a function of environment 2. Galaxy colour distributions 3. Galaxy SFR distributions 4. Interpretation 5. Large-scale structure dependence 6. Conclusions Morphology-Density Relation The “Outskirts” of clusters Clusters Field Where does the transition begin, and what causes it? E S0 Spirals Dressler 1980 High concentration clusters Low concentration (non-relaxed) Dressler 1980 Groups Postman & Geller 1984 • Morphology-density relation holds for irregular clusters, centrally-concentrated clusters, and groups • Therefore it is local galaxy density that is of most interest, not global cluster properties • Possibly additional effects in innermost regions (Whitmore et al., Dominguez et al.) SFR-Density relation R>2R200 Clusters Field Field critical density? 2dFGRS: Lewis et al. 2003 SDSS: Gomez et al. 2004 Empirical questions 1. How best to characterise galaxy population? • • 2. morphology, colour, SFR, or luminosity? how to quantify distribution (mean/median etc.) How to define environment observationally? • • • • clustercentric distance? projected galaxy density? 3-dimensional density? dark matter density (Gray et al.)? cluster type/mass? Outline 1. Background: Galaxy properties as a function of environment 2. Galaxy colour distributions 3. Galaxy SFR distributions 4. Interpretation 5. Large-scale structure dependence 6. Conclusions Colours • morphology is difficult to quantify – Especially to distinguish E from S0 • colours simple and direct tracer of SF (also metallicity, dust) • Sloan Digital Sky Survey – digital ugriz photometry and redshifts for nearby galaxies – use “model magnitudes” which give high S/N, centrallyconcentrated colours • density: – projected distance to 5th nearest neighbour – 3D density based on convolution with Gaussian kernel – cluster velocity dispersion Colour-magnitude relation Sloan DSS data Baldry et al. 2003 (see also Hogg et al. 2003) Blue Fraction Margoniner et al. 2000 De Propris et al. 2004 (2dFGRS) Analysis of colours in SDSS data: • Colour distribution in 0.5 mag bins can be fit with two Gaussians • Mean and dispersion of each distribution depends strongly on luminosity • Dispersion includes variation in dust, metallicity, SF history, and photometric errors (u-r) Baldry et al. 2004 Density Dependence Bright Lowest Densities • 23520 galaxies from SDSS DR1. magnitude limited with z<0.08 • density estimates Mr<-20 based on • keep mean and dispersion fixed at Baldry et al. (2004) values Faint • Fit height of two distributions to different density bins Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters Density Dependence Faint Bright 3X denser • 2 Gaussian model still a good fit • mean/dispersion of each population shows no strong dependence on density Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters Density Dependence Faint Bright 3X denser • 2 Gaussian model still a good fit • mean/dispersion of each population shows no strong dependence on density Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters Density Dependence Bright 3X denser “Infall regions” • mean/dispersion of each population shows no strong dependence on density Faint • Some evidence for a departure from the 2Gaussian model Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters Density Dependence Bright Highest density • mean/dispersion of each population shows no strong dependence on density Faint • Some evidence for a departure from the 2-Gaussian model Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters • Red sequence independence on environment has been known for a long time (e.g. Sandage & Visvanathan 1978) • But the insensitivity of blue mean and dispersion to environment is surprising: Properties of star-forming galaxies depend only on internal structure of galaxy Clusters do not inhibit SF in all blue galaxies • Fraction of red galaxies depends strongly on density. This is the primary influence of environment on the colour distribution. • Use cluster catalogue of Miller, Nichol et al. (C4 algorithm) • No dependence on cluster velocity dispersion observed. Local density is the main driver Outline 1. Background: Galaxy properties as a function of environment 2. Galaxy colour distributions 3. Galaxy SFR distributions 4. Interpretation 5. Large-scale structure dependence 6. Conclusions Ha distribution • Use Ha equivalent widths from SDSS and 2dFGRS (volumelimited samples Mr<-20) • Ha distribution also shows a bimodality • Star-forming W(Ha)>4 Å galaxies Balogh et al. 2004 (MNRAS 348, 1355) with The star-forming population • Amongst the star-forming population, there is no trend in Ha distribution with density • Trends of mean or median with density can be misleading • Hard to explain with simple, slow-decay models (e.g. Balogh et al. 2000) Correlation with density 2dFGRS • The fraction of starforming galaxies varies strongly with density • Correlation at all densities; still a flattening near the critical value • Fraction never reaches 100%, even at lowest densities Isolated Galaxies All galaxies Bright galaxies • Selection of isolated galaxies: – non-group members, with low densities on 1 and 5.5 Mpc scales • ~30% of isolated galaxies show negligible SF – environment must not be only driver of evolution. Outline 1. Background: Galaxy properties as a function of environment 2. Galaxy colour distributions 3. Galaxy SFR distributions 4. Interpretation 5. Large-scale structure dependence 6. Conclusions • Departures from 2-Gaussian model in dense regions might indicate a transforming population • Start with colour distribution in the lowest density regions • Transform galaxies from blue to red at uniform rate over a Hubble time Instantaneous truncation • If SFR is truncated instantly, result is similar to 2-Gaussian model • This is because: • 1. Colour evolution is rapid after truncation 2. Number of galaxies caught in transition at present day is small Short-timescale truncation could be important at all luminosities and densities Strangulation models • Slower SFR decay begins to populate intermediate colour regime Strangulation models • Slower SFR decay begins to populate intermediate colour regime Strangulation models • Slower SFR decay begins to populate intermediate colour regime • 2 Gyr timescale approximately what is expected if hot gas is stripped and galaxy allowed to consume cold gas supply at normal rate (Larson, Tinsley & Caldwell 1980; Balogh, Navarro & Morris 2000) • Not the only interpretation, but a successful model nonetheless GALFORM model • GALFORM is Durham model of galaxy formation (Cole et al. 2000) – parameters fixed to reproduce global properties of galaxies at z=0 (e.g. luminosity function) and abundance of SCUBA galaxies at high redshift • Use mock catalogues of 2dFGRS which include all selection biasses • Predict Ha from Lyman continuum photons, choose dust model to match observed Ha distribution • Assume hot gas is stripped from galaxies when they merge with larger halo (i.e. groups and clusters) which leads to strangulation of SFR (gradual decline) GALFORM predictions 1. Fraction of SF galaxies declines with increasing density as in data GALFORM predictions • Over most of the density range, correlation between stellar mass and SFR fraction is invariant Therefore SFR-density correlation is due to massdensity correlation • At highest densities, models predict fewer SF galaxies at fixed mass due to strangulation GALFORM predictions S5<0.2 Mpc-2 S5<0.2 Mpc-2 Observed Ha distribution independent of environment at all densities GALFORM predictions 1. 2. Fraction of SF galaxies declines with increasing density as in data At low densities, Ha distribution independent of environment GALFORM predictions 1. 2. Fraction of SF galaxies declines with increasing density as in data At low densities, Ha distribution independent of environment GALFORM predictions 1. 2. 3. Fraction of SF galaxies declines with increasing density as in data At low densities, Ha distribution independent of environment In densest environments, Ha distribution skewed toward low values GALFORM predictions Kauffmann et al. (2004) work with SDSS suggests correlation between SFR and stellar mass depends on environment. However this is not directly comparable in this form. Outline 1. Background: Galaxy properties as a function of environment 2. Galaxy colour distributions 3. Galaxy SFR distributions 4. Interpretation 5. Large-scale structure dependence 6. Conclusions Large scale structure r5.5 (Mpc-3) 0.050 0.010 0.005 Contours are lines of constant emission-line fraction • Emission-line fraction appears to depend on 1 Mpc scales and on 5.5 Mpc scales. Increasing fraction of Ha emitters 2dFGRS data. Similar results for SDSS GALFORM predictions: LSS Data r5.5 (Mpc-3) r5.5 (Mpc-3) Model r1.1 (Mpc-3) GALFORM predictions: LSS • Fraction of star-forming galaxies depends primarily on local density, but there is a further weak correlation with large scales • Not expected in CDM models because halo merger history depends only on local environment (Kauffmann et al. 1994) • Should be independently confirmed but suggests an important element missing from these models Conclusions • SFR/colour distribution among active population is independent of environment • Fraction of SF/blue galaxies decreases with increasing density • At low densities this trend may be due to change in mass function with environment • At high densities (~infall regions of clusters) there is evidence for a slowly transforming population. Details differ from GALFORM models • Evidence for dependence on large-scale densities that is not anticipated by models