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: 1.Observed clustering of galaxies 2.Observed evolution in the stellar mass function and the intracluster light (ICL) 3.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. Luminosity dependent clustering  is a power- law, but why?? Galaxy Clustering: I Davis et al ‘88 brighter fainter dP = n [1+  (r)] dV Definition of  

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

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

7. Dark Matter Clustering 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  CDM Simulation

8. Halo Clustering 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? Colin et al ‘99 The Big Question

9. The Model

10.  CDM Cosmology:  m =0.3,   =0.7,   =0.9, h=0.7 –ART N-body code (Klypin & Kravtsov) Box Sizes: 80 & 120 Mpc/h. N part = 512 3 Particle Mass: 3.2 x 10 8 & 1.1 x 10 9 M sun /h h peak =1-2 kpc/h Halos identified using BDM algorithm Simulation Details

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 Time Wechsler et al ‘02 (for a distinct halo)

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

14. Connecting Halos to Galaxies: I Find a relation between galaxy luminosity and halo V max, the maximum of the circular velocity function: [GM(<r)/r] 1/2, or, equivalently, M vir. 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 V max Correlates with Luminosity? For distinct halos, we use V max measured at z = z obs. For subhalos we use V max at the epoch of accretion: Time Accretion epoch V max, mass Why? V max at accretion should more accurately reflect the build-up of stellar mass, and hence luminosity

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

17. Results: Comparing Galaxy Clustering to Halo Clustering

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

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

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

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

22. 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. Are We Missing Satellites?

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 N sat increases for lower  8 M r <-21 = dashed M r <-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 V max 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 M halo size clumps Log(M vir )

28. Observations of the ICL 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” Gonzalez et al. 2005 Semi-major axis (kpc) Surface brighness (mag/arsec 2 ) deV

29. Modeling Stellar Mass Build-up 1)Use the observed z=1 galaxy stellar MF to connect stellar mass to halo mass at z=1 2)Follow the build-up of stellar mass with time using halo merger trees. 3)Ignore star-formation and other dissipative physics Appropriate for the most massive galaxies where z form,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: 1)Keep the satellite galaxy KeepSat 2)Put the satellite’s stars into the BCG Sat2Cen 3)Put the satellite’s stars into the ICL Sat2ICL 4)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. Observationally allowed range Sat2Cen

32. BCG Luminosity - Mass Relation M star / L K = 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 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. Gonzalez et al. 2005

34. Model Sat2ICL is the only model that matches an array of observations In massive halos (>10 13.5 M sun ), 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!) Implications

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 10 13.5 M sun have had little star-formation At lower masses, fraction of stars formed since z=1 decrease with increasing halo mass.

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

37. Evolution in M star -M vir Relation: II Log(M vir ) Log(M star ) Fraction of baryons stars 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).

38. The LRG Multiplicity Function

39. Observed LRG Multiplicity Function 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 M vir > 10 14 M sun LRGs identified in SDSS with photo-z’s (dz~0.03) Shirley Ho, et al. in prep

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

41. Conclusions 1)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. 2)Observations which are thought to evolve dissipationlessly with time are particularly attractive because they are easy to model and yet much can be learned. 3)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 M vir and M star, what effect does this have?

44. The Importance of Scatter: I 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 z = 0

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

46. bla The Importance of Scatter: III