Max Planck Institute for Astronomy - Annual Report 2005

76 III. Scientific Work
Mocking the Universe
Another powerful application of the CLF is the
construction of mock galaxy redshift surveys (hereafter
MGRSs), which are extremely useful tools for the interpretation
of large redshift surveys. As with any dataset,
several observational biases hamper a straightforward
interpretation of such surveys. The CLF is ideally
suited for building up virtual Universes from which
mock galaxy redshift surveys can be constructed using
the same biases and incompleteness effects as in the
real data. All that is required is a numerical simulation
of the dark matter distribution in the Universe. After
identifying the dark matter haloes in such a simulation,
the CLF can be used to populate each of these haloes
with galaxies of different luminosities. Note that, by
construction, the abundance and clustering properties
of these galaxies will automatically match those of
the data. After introducing a virtual observer in the
simulated volume, one can construct MGRSs, which
can be compared to real redshift surveys, such as the
2dFGRS and the Sloan Digital Sky Survey (SDSS) on
a one-to-one basis.
Fig. III.3.3 shows a slice through one of our virtual
Universes. Clockwise, from the upper left panel, we
plot the distribution of dark matter, of all galaxies,
of early-type galaxies, and of late-type galaxies. Note
that, at first sight, the galaxies seem to accurately trace
the dark matter mass distribution. However, a more
detailed analysis would reveal that the actual spatial
distributions of galaxies of different types and different
luminosities are statistically different from each
other, and from that of the dark matter. This reflects the
complicated dependence of the galaxy bias on scale,
type, and luminosity, which is nevertheless completely
specified by the CLF.
�� [km/s]
1000
0
–1000
N host = 8132
N sat = 12 569
8.5 9 9.5
log(L host / (h –2 L � ))
10 10.5 11
Satellite Kinematics
As discussed above, and as shown in Fig. III.3.1, the
observed clustering of galaxies as function of luminosity
puts tight constraints on how galaxies of different luminosities
occupy haloes of different masses. In order to
test the inferred relation between light and mass shown
in the lower left panel of Fig. III.3.1, we have used the
kinematics of satellite galaxies. Satellite galaxies are
those galaxies in a dark matter halo that do not reside at
the center of the halo (which are called central galaxies),
but which instead orbit within the halo at relatively large
halo-centric radii. Consequently, these satellites probe
the potential well out to the outer edges of their haloes,
and are therefore ideally suited to measure the total halo
masses. In particular, the typical velocity with which
satellites orbit their corresponding central galaxy is a
direct, dynamical indicator of the mass of the associated
halo. A downside of this method, however, is that the
number of detectable satellites in individual systems
is generally much too small to obtain a reliable mass
estimate. However, one can stack the data on many
central-satellite pairs to obtain statistical estimates of the
halo masses associated with central galaxies of a given
luminosity. The crucial problem is how to decide which
galaxy is a central galaxy, and which galaxy a satellite.
Using the MGRSs describes above, we optimized the
central-satellite selection criteria to yield large numbers
log(�� sat � / km/s))
Fig. III.3.4: Left: The observed velocity differences ΔV between
host and satellite galaxies in the 2dFGRS as function of the
luminosity of the host galaxy. Right: The satellite velocity
dispersion as function of host luminosity. Solid dots with errorbars
correspond to the 2dFGRS results shown in the left panel,
while the gray area indicates the expectation values obtained
from the CLF.
2.5
2
CLF-predictious
2dFGRS
1.5
8.5 9 9.5
log(L host / (h –2 L � ))
10 10.5 11