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The Coevolution of Galaxies and Dark Matter Halos

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Title: The Coevolution 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 ltlt Uncertainties in
    galaxy formation/evolution

3
The Clustering of Galaxies and Halos from z4
to z0
4
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
Galaxy Clustering III
  • Clustering at z1
  • Clustering at z4

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 ?m0.3, ??0.7, ??0.9, h0.7
  • 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
    hpeak1-2 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(ltr)/r1/2, 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
Connecting Halos to Galaxies II
ngal(gtL) nhalo(gtVmax)
r-band luminosity
Vmax
17
ResultsComparing Galaxy Clustering to Halo
Clustering
18
z 0
SDSS data
  • Data red points
  • Halos blue lines
  • DM dotted lines

Notice the bump
Projected correlation function
Data Zehavi 05
19
Vmax at accretion vs. Vmax today
  • In order to match observed clustering at z0, we
    must use accretion epoch Vmax for subhalos
  • Using accretion epoch effectively increases the
    fraction of galaxies that are satellites

20
z 1
DEEP2 data
  • Data red points
  • Halos blue lines
  • DM dotted lines

Data Coil 06
21
z 4
Subaru data
Notice strong break on small scales
Notice strong linear bias b5
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 (gt20) 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

Mrlt-21 dashed Mrlt-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 z1
26
Evolution in the Stellar Mass Function
  • Observations indicate mild/no evolution in the
    stellar MF since z1 at the massive end
  • Evolution in the LF also consistent with passive
    evolution at the bright end since z1.

?M-4 0.2dex
27
Evolution in the Halo Mass Function
  • Strong evolution in the Halo MF from z1 to z0
    at the massive end
  • Growth of halo does not track growth of central
    galaxy at zlt1 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 z1 galaxy stellar MF to connect
    stellar mass to halo mass at z1
  • 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,starsgt2
  • 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 satellites stars into the BCG Sat2Cen
  • Put the satellites stars into the ICL
    Sat2ICL
  • Equally split between 2) and 3)
    Sat2CenICL

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 Sat2CenICL marginally ruled out.
  • Implies that lt50 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 BCGICL luminosities.
  • Model KeepSat (blue points) dramatically fails
    this test.
  • We assumed that ICL is built-up at zlt1 by major
    mergers, tidal stripping not important.
  • Validated by hydro-sims.
  • Model Sat2ICL (red points) reproduces observed
    ICL light fraction better than model Sat2CenICL
    (blue points).
  • Depends on modeling of observed surface
    brightness profile and defn of ICL.

34
Implications
  • Model Sat2ICL is the only model that matches an
    array of observations
  • In massive halos (gt1013.5 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
    (BCGICL) light since z1 (very hard to observe
    this at z1!)

35
Implications for Star-formation
  • Match z0 stellar MF to the z0 halo MF in the
    usual way
  • Compare z0 true stellar mass to the z0
    stellar mass predicted by our dissipationless
    models
  • The difference should reflect the amount of
    star-formation since z1
  • Galaxies in halos above 1013.5 Msun have had
    little star-formation
  • At lower masses, fraction of stars formed since
    z1 decrease with increasing halo mass.

36
Evolution in Mstar-Mvir Relation I
Evolution in galaxy stellar MF measured out to
z4 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
    ?80.75 (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 0.2ltzlt0.5 in
    Rosat/Chandra x-ray data that overlap SDSS
    footprint.
  • Cluster masses determined from x-ray
    observations
  • Mvir gt 1014 Msun
  • LRGs identified in SDSS with photo-zs (dz0.03)

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 MLRGgt6E12 MLRGgt1E13
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 zlt1.
    LRG multiplicity function provides information
    about merger/DF timescales.

42
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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

47
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