1. The role of environment on galaxy evolution University of Durham Michael Balogh University of Waterloo (Canada)

2. Collaborators Richard Bower, Simon Morris, Dave Wilman No picture: Vince Eke, Cedric Lacey, Fumiaki Nakata Durham Ivan Baldry & Karl Glazebrook Johns Hopkins Baugh, Cole, Frenk (Durham) Bob Nichol, Chris Miller & Alex Gray Carnegie Mellon John Mulchaey & Gus Oemler OCIW Ray Carlberg Toronto Ian Lewis (Oxford) and the 2dFGRS team No picture: Taddy Kodama

3. Outline 1.Background 2.Low redshift: SDSS and 2dFGRS 3.GALFORM predictions 4.Groups and clusters at z~0.5 5.Conclusions

4. Outline 1.Background 2.Low redshift: SDSS and 2dFGRS 3.GALFORM predictions 4.Groups and clusters at z~0.5 5.Conclusions

5. E Morphology-Density Relation Dressler 1980 Clusters Field S0 Spirals

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

7. Nature or Nurture? Nature? Elliptical galaxies only form in protoclusters at high redshift. Rest of population is due to infall. or Nurture? Galaxy evolution proceeds along a different path within dense environments.

8. Theoretical expectations (?) Isolated galaxies have (invisible) halo of hot gas that can cool and replenish the disk –allows star formation to continue for longer Cluster galaxies lose this gas, so their SFR declines more quickly. Also cluster galaxies form earlier. –therefore SFRs should be lower in clusters no ram-pressure stripping, harassment needed to achieve reasonable match to observed clusters (Diaferio et al. 2001; Okamoto et al. 2003).

9. Butcher-Oemler effect Concentrated clusters at high redshift have more blue galaxies than concentrated clusters at low redshift Butcher & Oemler (1984)

10. Butcher-Oemler effect A lot of scatter –appears to be mostly due to correlation with cluster richness still room to worry about cluster selection? Margoniner et al. (2001)

11. Butcher-Oemler effect SDSS: Goto et al. (2003) Many of blue galaxies turned out to have post- starburst spectra (Dressler & Gunn 1992; Couch & Sharples 1987) Suggested nurture: –ram-pressure stripping (Gunn & Gott 1972) –tidal effects (Byrd & Valtonen 1990) –harassment? (Moore et al. 1999)

12. But: Field galaxy evolution But field population also evolves strongly (Lilly et al. 1996) Post-starburst galaxies equally abundant in the field (Zabludoff et al. 1996; Goto et al. 2003) So: does BO effect really point to cluster-specific physics, or just the evolving field and infall rate (Ellingson et al. 2001) ? Steidel et al. (1999)

13. Observations: z~0.3 Strangulation model: – infall rate + assumed decay rate of star formation => radial gradient in SFR Radial gradients in CNOC clusters suggest  ~2 Gyr Balogh, Navarro & Morris (2000) Ellingson et al. (2001) higher rate of infall at high redshift leads to steeper gradients

14. Some remaining questions Where does the morphology-density relation start? i.e. what environment drives transformations? What is the timescale for this transformation? e.g. Strangulation (slow) or harassment (fast) How does SF evolve with time in different environments? Can environment-related transformations drive the Madau plot?

15. Outline 1.Background 2.Low redshift: SDSS and 2dFGRS 3.GALFORM predictions 4.Groups and clusters at z~0.5 5.Conclusions

16. 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? cluster type/mass?

17. 2dFGRS and SDSS 2dFGRS: 250k redshifts, photographic plates SDSS (DR1): 200k redshifts, digital ugriz imaging morphology is difficult to quantify –Especially to distinguish E from S0 –use colours and H  equivalent widths as tracer of SFR density: –projected distance to 5 th nearest neighbour –3D density based on convolution with Gaussian kernel –cluster velocity dispersion

18. Median SFR-Density relation R>2R 200 2dFGRS: Lewis et al. 2002 SDSS: Gomez et al. 2003 critical density? Field Clusters

19. H  distribution H  distribution shows a bimodality: mean/median of whole distribution can be misleading Balogh et al. 2004

20. H  distribution H  distribution shows a bimodality: mean/median of whole distribution can be misleading Isolate star-forming galaxies with W(H  )>4 Å Balogh et al. 2004

21. The star-forming population Amongst the star- forming population, there is no trend in H  distribution with density Hard to explain with simple, slow-decay models (e.g. Balogh et al. 2000)

22. Correlation with density The fraction of star- forming galaxies varies strongly with density 2dFGRS

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. 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  1.1 (Mpc -3 )

26. Colours: SDSS Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters

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

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

29. Baldry et al. 2003 (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 Bright Faint

30. 24346 galaxies from SDSS DR1. magnitude limited with z<0.08 density estimates based on M r <-20 Balogh, Baldry, Nichol, Miller, Bower & Glazebrook, submitted to ApJ Letters

31. Fraction of red galaxies depends strongly on density. This is the primary influence of environment on the colour distribution. Density-dependence stronger than luminosity. Bright and faint galaxies show trend with density Use cluster catalogue of Miller, Nichol et al. (C4 algorithm) No strong dependence on cluster velocity dispersion observed. Local density is the main driver

32. Mean colour of distribution is only a weak function of density, but depends strongly on luminosity. Separates internal/external influences trend may mean galaxies in low- density regions have more recent star formation, on average but not likely related to large population of red galaxies in clusters

33. How rapid must the blue  red transition be? colour evolves rapidly if timescale for star formation to stop is short if transformations occur uniformly in time: need  <0.5 Gyr if transformations are more common in the past, longer timescales permitted Blue Peak Red Peak

34. Summary: SDSS & 2dFGRS Colour and H  distributions suggest any transformations must have a short timescale SFH depends on environment and galaxy luminosity (mass) in a separable way.

35. Outline 1.Background 2.Low redshift: SDSS and 2dFGRS 3.GALFORM predictions 4.Groups and clusters at z~0.5 5.Conclusions

36. 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)

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

38. 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

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

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

42. 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

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

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

45. GALFORM predictions At low densities, models work very well - At higher densities, data require more rapid transition than predicted 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 (Lemson & Kauffmann 1999) - Should be independently confirmed but suggests an important element missing from these models

46. Outline 1.Background 2.Low redshift: SDSS and 2dFGRS 3.GALFORM predictions 4.Groups and clusters at z~0.5 5.Conclusions

47. Evolution - expectations To (over)simplify the issue: Nature: Evolution in groups, clusters should parallel evolution in the field Nurture: SFR depends only on environment – so no evolution with redshift

48. Cluster galaxy evolution Nakata et al. in prep CNOC clusters: Evolution in SFR different from colour? Suggests high blue fraction at high redshift due to increased infall rate 2dF CNOC

49. Cluster galaxy evolution Kodama et al. in prep Couch et al. 2001 Balogh et al. 2002 Fujita et al. 2003 Tresse et al. 2002 Complete H  studies: Even at z=0.5, total SFR in clusters lower than in surrounding field Field z~0.3 z~0.5

50. Cluster galaxy evolution Complete H  based SFR estimates Evolution in total SFR per cluster not well constrained considerable scatter of unknown origin systematic uncertainties in mass estimates make scaling uncertain Kodama et al. in prep Finn et al. 2003

51. Cluster galaxy evolution Complete H  based SFR estimates Evolution in total SFR per cluster not well constrained considerable scatter of unknown origin systematic uncertainties in mass estimates make scaling uncertain Kodama et al. in prep Finn et al. 2003

52. Evolution in groups z~0.05: 2dFGRS (Eke et al. 2004) –Based on friends-of-friends linking algorithm –calibrated with simulations. Reproduces mean characteristics (e.g. velocity dispersion) of parent dark matter haloes z~0.45: CNOC2 (Carlberg et al. 2001) –selected from redshift survey, 0.3<z<0.55 –Cycle 12 HST imaging + deeper spectroscopy with LDSS2-Magellan

53. Group comparison Wilman et al. in prep Fraction of non-SF galaxies Use [OII] equivalent width to find fraction of galaxies without significant star formation most galaxies in groups at z~0.4 have significant star formation – in contrast with local groups

54. Wilman et al. in prep Fraction of non-SF galaxies increases with redshift Group Evolution Fraction of non-SF galaxies

55. Wilman et al. in prep Fraction of non-SF galaxies increases with redshift for both groups and field Field Evolution Fraction of non-SF galaxies

56. Outline 1.Background 2.Low redshift: SDSS and 2dFGRS 3.GALFORM predictions 4.Groups and clusters at z~0.5 5.Conclusions

57. Conclusions: What have we learned? From the local colour/SFR distribution: –transformations must either be rapid, or occur preferentially at high redshift –simple strangulation model does not work –SFR depends weakly on large-scale structure From comparison with clusters/groups at higher redshift: –evolution may be stronger in groups/field than in clusters

58. The End

59. Wilman et al. in prep shape of [OII] distribution evolves with redshift but does not depend on environment

60. Wilman et al. 2004 CNOC2 field lacks significant population of galaxies without star formation [OII] distribution in groups looks similar at both redshifts, with some evolution

63. 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.