Annual Report 2011 Max Planck Institute for Astronomy

Fig. III.3.4: hubble Space Telescope image of the compact lenticular
galaxy NGC 1277, the host of an “übermassive” black
hole. The image size is 19 8 kpc. The galaxy has a half-light
radius of 1 kpc, is strongly flattened, and is disky. North is up
and East is to the left.
power-law index is decidedly below unity, in contrast
to early (and widely adopted) studies. We find it likely
to be not as “fundamental” as previously thought, while
L tot poses an equivalently strong predictor of M .
Our group also is strongly involved in expanding our
knowledge of the SMBH scaling relation with respect to
the highest SMBH masses. This part of the scaling relations
is of special interest not only because it is hitherto
sparsely sampled. There is also doubt in the scientifc
community concerning the validity of the power law established
for the intermediate-mass range of SMBHs. We
here specifically investigate compact galaxies with unusually
high velocity dispersion, and conducted a dedicated
spectroscopic survey on the Hobby-Eberly Telescope
in order to find such galaxies and single out objects
most promising to allow the measurement of M . We
subsequently acquired integral-field spectroscopic data
of the resulting sample, using PPAK on the Calar Alto
3.5 m telescope. We were able to detect a SMBH with a
mass far exceeding the prediction of current scaling relations,
constituting 14 % (instead of 0.2 %) of the
host (bulge) mass. This peculiar galaxy is shown in Fig.
III.3.4. Its “übermassive” SMBH may be interpreted as
a statistical outlier of the currently adopted scaling relations,
but nevertheless demands a corresponding formation
scenario. Whether the latter is feasible within the
hitherto proposed galaxy-SMBH co-evolution models is
currently unclear. As a consequence, there may be more
than one principal SMBH formation channel and the established
scaling relations may thus not be universal.
What next? Lighting up the dark in the Local Group
For objects in the Local Group – that is our own Milky
Way, sister galaxy Andromeda (M 31) and their globular
clusters and dwarf galaxy satellites – we are in the very
III.3 Dynamics of Galaxies: inferring their mass distribution and formation history 61
Credit: Glenn van de Ven
fortunate position of being able to measure photometric
and spectroscopic quantities for individual stars, often to
very high precision, thanks to both their proximity and
the advances in modern observing techniques. These data
include not only line-of-sight velocities, but motions
in the plane of the sky (proper motions) and metal abundances.
Having the full 3D velocity information means
we can directly calculate the velocity anisotropy, and
thus break the shape-mass-anisotropy degeneracy (see
Fig. III.3.5). Furthermore, by studying separately the dynamics
of chemically different stellar populations, we
can recover the formation history of these objects.
The hubble Space Telescope (HST) has delivered
photometric and proper motion data of exceptional accuracy
and also ground-based facilities provide remarkable
data-sets of line-of-sight velocities and metal abundances,
as well as proper motions. Large-scale surveys may
lack the sensitivity of the HST but are a vital tool due to
the sheer number of stars that they have observed; surveys
such as hipparcos, SDSS, and the RAdial Velocity
Experiment (rave) have provided a wealth of information
over large regions of the sky, which have been used
to good effect in studies of the Milky Way. For studying
the smaller denizens of the Local Group, there have also
been a number of focused observing efforts; for example
thousands of line-of-sight velocities have been published
for four of the Milky Way’s classical dwarfs: cariNa,
forNax, sculptor and sextaNs. And the future is bright:
there are a number of surveys coming online over the
next few years that will expand the data sets that are currently
available. In particular, Gaia will provide distances,
velocities, metallicities and even age estimates of
unprecedented accuracy over the whole sky.
We must ensure we have tools in place both to analyse
the existing data and to fully exploit the upcoming
data. To this end, we are extending existing dynamical
modeling techniques to include proper motions as well
as line-of-sight velocities, and to directly fit discrete
data using maximum likelihood methods. Apart from
the loss of information when binning discrete data, the
likelihood method can also be extended easily to incorporate
further information. For example: typically, and
unavoidably in case of binning, contaminants (e.g. a