The Co-evolution of Galaxies and Dark Matter Halos

1. The Co-evolution of Galaxies and Dark Matter Halos Charlie Conroy (Princeton University) with Andrey Kravtsov, Risa Wechsler, Martin White, & Shirley Ho.

2. Outline • What we learn from: • Observed clustering of galaxies • Observed evolution in the stellar mass function and the intracluster light (ICL) • Observed multiplicity function of LRGs in groups and clusters • The Big Picture: • What does LCDM plus observations of galaxies tell us about the relation between galaxies and dark matter? • Uncertanties in cosmology << Uncertainties in galaxy formation/evolution

3. The Clustering of Galaxies and Halos from z=4 to z=0

4. Definition of  dP = n [1+(r)] dV Galaxy Clustering: I • Luminosity dependent clustering • is a power-law, but why?? brighter fainter Davis et al ‘88

5. Galaxy Clustering: II • Galaxy clustering is a function of luminosity, confirmed in both SDSS and 2dF surveys (among others) More clustered Definition of galaxy bias: b2 = galdm Brighter galaxies Note: bias measured at a particular scale Zehavi et al. ‘05

6. Ouchi et al. ‘05 ~50-70 kpc/h ~1-10 Mpc/h Galaxy Clustering: III • Clustering at z~4 • Clustering at z~1 Coil et al ‘06 More clustered Brighter galaxies Brighter galaxies

7. Dark Matter Clustering CDM Simulation • Galaxy clustering is ~ a power-law and evolves only weakly with redshift • DM clustering is not a power law, and is a strong function of redshift • How do we reconcile this with observed galaxy clustering? Colin et al ‘99

8. Halo Clustering Colin et al ‘99 • The clustering of dark matter halos is similar to the clustering of galaxies (i.e. power-law, slow function of redshift) • (How) do galaxies correspond to dark matter halos? The Big Question

9. The Model

10. Simulation Details • CDM Cosmology: m=0.3, =0.7, =0.9, h=0.7 • ART N-body code (Klypin & Kravtsov) • Box Sizes: 80 & 120 Mpc/h. Npart = 5123 • Particle Mass: 3.2 x 108 & 1.1 x 109 Msun/h hpeak=1-2 kpc/h • Halos identified using BDM algorithm

11. Distinct halos vs. Subhalos “Distinct” halos Subhalos: their centers are within the virial radii of larger “parent” halos Note: subhalos used to be distinct halos!

12. Merger Trees (for a distinct halo) Time Wechsler et al ‘02

13. Halo Evolution Distinct Halo Evolution: Subhalo Evolution: Accretion epoch Constant increase increase decrease Vmax, mass Vmax, mass Time Time

14. Connecting Halos to Galaxies: I • Find a relation between galaxy luminosity and halo Vmax, the maximum of the circular velocity function: [GM(<r)/r]1/2, or, equivalently, Mvir. • Why? • Tully-Fisher & Faber-Jackson relations demonstrate a strong correlation between galaxy velocity and luminosity. • Strong theoretical expectations that galaxy luminosity will correlate with halo mass (e.g. White & Rees ‘78) • brighter galaxies live in more massive halos

15. Which Vmax Correlates with Luminosity? • For distinct halos, we use Vmax measured at z = zobs. • For subhalos we use Vmax at the epoch of accretion: Accretion epoch Why? Vmax at accretion should more accurately reflect the build-up of stellar mass, and hence luminosity Vmax, mass Time

16. A one-to-one mapping between luminosity and Vmax such that the observed luminosity function is preserved. The LF is the only input to the model. z=0 L-Vmax relation Note: no scatter Connecting Halos to Galaxies: II ngal(>L) = nhalo(>Vmax) r-band luminosity Vmax

17. Results:Comparing Galaxy Clustering to Halo Clustering

18. SDSS data z ~ 0 Notice the “bump” Data = redpoints Halos = bluelines DM = dotted lines Projected correlation function: Data: Zehavi ‘05

19. accretion today Vmax at accretion vs. Vmax today • In order to match observed clustering at z=0, we must use accretion epoch Vmax for subhalos • Using accretion epoch effectively increases the fraction of galaxies that are satellites

20. DEEP2 data z ~ 1 Data = red points Halos = blue lines DM = dotted lines Data: Coil ‘06

21. Subaru data z ~ 4 Notice strong “break” on small scales Notice strong linear bias: b~5 Data: Ouchi ‘05

22. Are We Missing Satellites? • We have assumed that a satellite galaxy is destroyed when the subhalo is destroyed • Are there satellite galaxies which have no counterparts in (our) simulations?? • No. • Significant fraction (>20%) of “missing” subhalos ruled out observationally, for the mass ranges we probe • In other words, subhalos in our simulations do not experience significant overmerging.

23. What About 8? • The 2-pt auto-correlation of halos in this model does not depend on 8 • Large scale clustering of halos decreases, but Nsat increases for lower 8 Mr<-21 = dashed Mr<-20.5 = solid

24. Implications • gal is a power-law because halo is a power-law • deviations from a power-law at high z and high luminosity are due to the clustering of halos (incl subhalos). • High-res dissipationless N-body simulations can completely describe & explain the dependence of galaxy clustering on luminosity, scale, and redshift with a simple assumption regarding the relation between galaxy luminosity and Vmax • Understanding luminosity & scale dependent clustering is separable.

25. Build-up of stellar mass and the ICL since z=1

26. Evolution in the Stellar Mass Function • Observations indicate mild/no evolution in the stellar MF since z=1 at the massive end • Evolution in the LF also consistent with ~passive evolution at the bright end since z=1. M-4 ~ 0.2dex

27. Evolution in the Halo Mass Function • Strong evolution in the Halo MF from z=1 to z=0 at the massive end • Growth of halo does not track growth of central galaxy at z<1 in massive halos • But halos accrete most of their mass in ~1/10 Mhalo size clumps Log(Mvir)

28. Observations of the ICL Gonzalez et al. 2005 • BCG surface brightness profiles in excess of deVaucouleurs at large scales • Best fit by a 2-compenent deV profile, rather than a generalized Sersic profile • Associate 2nd deV profile with “ICL” Surface brighness (mag/arsec2) deV Semi-major axis (kpc)

29. Modeling Stellar Mass Build-up • Use the observed z=1 galaxy stellar MF to connect stellar mass to halo mass at z=1 • Follow the build-up of stellar mass with time using halo merger trees. • Ignore star-formation and other dissipative physics • Appropriate for the most massive galaxies where zform,stars>2 • Therefore a lower bound to stellar mass build-up

30. Fate(s) of Satellite Galaxies • The evolution of satellite galaxies is tracked along with its dark matter subhalo until the subhalo dissolves. When the subhalo dissolves we have a decision to make: • Keep the satellite galaxy KeepSat • Put the satellite’s stars into the BCG Sat2Cen • Put the satellite’s stars into the ICL Sat2ICL • Equally split between 2) and 3) Sat2Cen+ICL model name

31. Evolution in the Galaxy Stellar MF • Sample the observational uncertainties • Model Sat2Cen ruled out by observed evolution in stellar MF. • Other models OK. Sat2Cen Observationally allowed range

32. BCG Luminosity - Mass Relation • Mstar / LK = 0.72 • Model Sat2Cen ruled out (again). • Model Sat2Cen+ICL marginally ruled out. • Implies that <50% of satellites from disrupted subhalos deposit their stars onto the central BCG

33. The Intracluster Light Gonzalez et al. 2005 • Model Sat2ICL (red points) reproduces observed total BCG+ICL luminosities. • Model KeepSat (blue points) dramatically fails this test. • We assumed that ICL is built-up at z<1 by major mergers, tidal stripping not important. • Validated by hydro-sims. • Model Sat2ICL (red points) reproduces observed ICL light fraction better than model Sat2Cen+ICL (blue points). • Depends on modeling of observed surface brightness profile and def’n of ICL.

34. Implications • Model Sat2ICL is the only model that matches an array of observations • In massive halos (>1013.5 Msun), satellite galaxies dissolve when their associated subhalo dissolves, and the satellite stars are dumped primarily into the ICL. • This explains the apparent contradiction between the lack of evolution in the stellar MF and the strong evolution in the halo MF. • This model predicts strong evolution in the total (BCG+ICL) light since z=1 (very hard to observe this at z=1!)

35. Implications for Star-formation • Match z=0 stellar MF to the z=0 halo MF in the usual way • Compare z=0 “true” stellar mass to the z=0 stellar mass predicted by our dissipationless models • The difference should reflect the amount of star-formation since z=1 • Galaxies in halos above 1013.5 Msun have had little star-formation • At lower masses, fraction of stars formed since z=1 decrease with increasing halo mass.

36. Evolution in Mstar-Mvir Relation: I Evolution in galaxy stellar MF measured out to z~4 by Fontana et al. 2006

37. Evolution in Mstar-Mvir Relation: II • Match stellar MF to halo MF at various epochs. • As Universe evolves, the peak conversion efficiency evolves to lower halo masses (“downsizing”) • At low halo masses stellar mass and halo mass increase with time, whereas at higher halo masses only the halo mass increases with time. • Simple model matches observations, and favors 8=0.75 (dashed line). Fraction of baryons stars Log(Mstar) Log(Mvir)

38. The LRG Multiplicity Function

39. Observed LRG Multiplicity Function Shirley Ho, et al. in prep • 43 Clusters identified at 0.2<z<0.5 in Rosat/Chandra x-ray data that overlap SDSS footprint. • Cluster masses determined from x-ray observations • Mvir > 1014 Msun • LRGs identified in SDSS with photo-z’s (dz~0.03)

40. Modeling the LRG Multiplicity Fcn t = 3.2 Gyr t = 1.6 Gyr t = time for LRG to merge once accreted Shape and normalization are important constraints t = 4.3 Gyr t = 5.9 Gyr Data MLRG>6E12 MLRG>1E13

41. Conclusions • By utilizing the observed number density of galaxies and LCDM simulations we can learn a great deal about the relation between galaxies and halos and the evolution of this relation with time. • Observations which are thought to evolve dissipationlessly with time are particularly attractive because they are easy to model and yet much can be learned. • The ICL is built up by merging satellites at z<1. LRG multiplicity function provides information about merger/DF timescales.

43. What about scatter? • We expect scatter between Mvir and Mstar, what effect does this have?

44. The Importance of Scatter: I z = 0 • Scatter strongly affects the clustering of bright galaxies, but does not affect fainter galaxies • Use this sensitivity to constrain the amount of scatter for bright galaxies • Work in progress

45. The Importance of Scatter: II Tasitsiomi et al. 04 • Scatter needed to match the observed galaxy-mass cross correlation function for bright galaxies • Note: these plots were made using the current Vmax for subhalos. We have not yet investigated scatter using accretion epoch Vmax for subhalos

46. The Importance of Scatter: III • bla