1. Clusters at low redshift University of Durham University of Waterloo (Canada) University of Durham Michael Balogh

2. Bob Nichol, Chris Miller & Alex Gray Carnegie Mellon Collaborators Richard Bower Durham Ivan Baldry & Karl Glazebrook Johns Hopkins Ian Lewis (Oxford) and the 2dFGRS team GALFORM people: Baugh, Cole, Lacey, Frenk Durham Vince Eke Durham

3. 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

4. E Morphology-Density Relation The “Outskirts” of clusters Dressler 1980 Clusters Field Where does the transition begin, and what causes it? S0 Spirals

5. Postman & Geller 1984 Dressler 1980 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.) High concentration clustersLow concentration (non-relaxed) Groups

6. SFR-Density relation R>2R 200 2dFGRS: Lewis et al. 2003 SDSS: Gomez et al. 2004 critical density? Field Clusters

7. Empirical questions 1.How best to characterise galaxy population? morphology, colour, SFR, or luminosity? how to quantify distribution (mean/median etc.) 2.How to define environment observationally? clustercentric distance? projected galaxy density? 3-dimensional density? dark matter density (Gray et al.)? cluster type/mass?

8. 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

9. 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, centrally- concentrated colours density: –projected distance to 5 th nearest neighbour –3D density based on convolution with Gaussian kernel –cluster velocity dispersion

10. Colour-magnitude relation Baldry et al. 2003 (see also Hogg et al. 2003) Sloan DSS data

11. Blue Fraction Margoniner et al. 2000 De Propris et al. 2004 (2dFGRS)

12. Baldry et al. 2004 (u-r) 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

13. Density Dependence 23520 galaxies from SDSS DR1. magnitude limited with z<0.08 density estimates based on M r <-20 keep mean and dispersion fixed at Baldry et al. (2004) values Fit height of two distributions to different density bins Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters Lowest Densities Bright Faint

14. Density Dependence 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 Bright Faint

15. Density Dependence 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 Bright Faint

16. Density Dependence 3X denser Bright Faint “Infall regions” mean/dispersion of each population shows no strong dependence on density Some evidence for a departure from the 2- Gaussian model Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters

17. Density Dependence Highest density mean/dispersion of each population shows no strong dependence on density Some evidence for a departure from the 2-Gaussian model Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters Bright Faint

18. 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

19. 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

20. 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

21. H  distribution Use H  equivalent widths from SDSS and 2dFGRS (volume- limited samples M r <-20) H  distribution also shows a bimodality Star-forming galaxies with W(H  )>4 Å Balogh et al. 2004 (MNRAS 348, 1355)

22. The star-forming population Amongst the star-forming population, there is no trend in H  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)

23. Correlation with density The fraction of star- forming galaxies varies strongly with density Correlation at all densities; still a flattening near the critical value Fraction never reaches 100%, even at lowest densities 2dFGRS

24. Isolated 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. All galaxies Bright galaxies

25. Group catalogues 2dFGRS (Eke et al.) –Based on friends-of-friends linking algorithm –calibrated with simulations. Reproduces mean characteristics (e.g. velocity dispersion) of parent dark matter haloes –is highly complete, at expense of having unphysical contamination, esp. at low masses –selected subsample with at least 10 members above our luminosity limit. SDSS (Nichol, Miller et al.) –Search for clustering in spatial and colour space; also calibrated with simulations –Selected subsample with Gaussian velocity dispersions –is a highly pure sample, at expense of being incomplete

26. 2dF groups SDSS groups circle size is proportional to virial radius (vel. dispersion)

27. Large scale structure Measured 3-d density on 1.1 and 5.5 Mpc scales groups are well- separated in this plane, by velocity dispersion ●  > 600 km/s ● 200 <  < 400

28. 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

29. Departures from 2-Gaussian model in dense regions might indicate a transforming population

30. Start with colour distribution in the lowest density regions Transform galaxies from blue to red at uniform rate over a Hubble time

31. 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

32. Strangulation models Slower SFR decay begins to populate intermediate colour regime

33. Strangulation models Slower SFR decay begins to populate intermediate colour regime

34. 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

35. 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 H  from Lyman continuum photons, choose dust model to match observed H  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)

36. GALFORM predictions 1.Fraction of SF galaxies declines with increasing density as in data

37. GALFORM predictions Over most of the density range, correlation between stellar mass and SFR fraction is invariant  Therefore SFR-density correlation is due to mass- density correlation At highest densities, models predict fewer SF galaxies at fixed mass due to strangulation

38. GALFORM predictions Observed H  distribution independent of environment at all densities  5 <0.2 Mpc -2

39. GALFORM predictions 1.Fraction of SF galaxies declines with increasing density as in data 2.At low densities, H  distribution independent of environment

40. GALFORM predictions 1.Fraction of SF galaxies declines with increasing density as in data 2.At low densities, H  distribution independent of environment

41. GALFORM predictions 1.Fraction of SF galaxies declines with increasing density as in data 2.At low densities, H  distribution independent of environment 3.In densest environments, H  distribution skewed toward low values

42. 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.

43. 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

44. Large scale structure Contours are lines of constant emission-line fraction Emission-line fraction appears to depend on 1 Mpc scales and on 5.5 Mpc scales.  5.5 (Mpc -3 ) 0.050 0.010 0.005 Increasing fraction of H  emitters 2dFGRS data. Similar results for SDSS

45. GALFORM predictions: LSS  5.5 (Mpc -3 )  1.1 (Mpc -3 ) Model Data

46. 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

47. 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