Annual Report 2011 Max Planck Institute for Astronomy

54 III. Selected Research Areas
Fig. III.2.6.: Relation between the non-thermal sonic Mach number
(ℳ s ) and the standard deviation of the column density PDF
in IRDCs (blue diamonds) and in nearby molecular clouds (red
diamonds). The sonic Mach number is a measure of turbulent
energy in the clouds and the standard deviation of column
density a measure of the magnitude of density fluctuations in
it. The correlation coefficient between these two parameters
describes how efficiently turbulence is inducing density fluctuations
in the cloud.
the IRDCs is difficult because of their distance. It would
be possible to build a census of low-mass star formation
in IRDCs using X-ray observations, and MPIA scientists
are involved in programs aiming at this.
Figure III.2.5 (bottom panel) shows the cumulative
PDFs for the same IRDCs. The panel also shows cumulative
PDFs of three nearby clouds: Orion A, Orion B,
and the California cloud. The figure shows that IRDCs
contain a relatively high amount of high-column density
material, even more than the most active nearby clouds
(Orion A). This may indicate that also star-forming rates
in the IRDCs will be significantly higher than in nearby
clouds.
The similarity of the PDFs of IRDCs with star-forming
low-mass clouds suggests that the pressure-confined
structures that were earlier hypothesized to be a
pre- requisite for star formation have already formed in
IRDCs. If so, it would indeed appear that the requirement
for pressure-confined structures can form a paradigm
that covers the entire mass-spectrum of star formation.
However, confirming this requires further studies
of the kinematic structure in IRDCs that could connect
pressure to the shapes of the column density PDFs.
These studies will become possible once the combined
Nir Mir mass distribution mapping technique will be
applied to a large set of IRDCs.
Another interesting application of the high-dynamicrange
column density data is using them to determine
the total column density variance in molecular clouds.
As described earlier, the density fluctuations in molecular
clouds are induced by the turbulent motions in the
clouds. The magnitude of these fluctuations, or in other
words the total density variance, σ(ρ), is expected to depend
on the total turbulent energy: ℳ s b σ(ρ). In
this relation, ℳ s stands for the non-thermal sonic Mach
number, which is a measure of the non-thermal kinetic
(assumably, turbulent) energy in the cloud. The coefficient
b is a constant that describes the strength of the
coupling of the two parameters. The relation is particularly
important, because the star formation theories that
use the density PDF as a measure of density statistics use
this to scale the density fluctuations based on the turbulent
energy content in the cloud.
Figure III.2.6 shows tentatively the first direct measurement
of the ℳ s b σ(ρ) relation in molecular
clouds, performed by Kainulainen & Tan (submitted).
The total density variance for the plot was measured us-
sln N / N
2.5
2
1.5
1
0.5
Region: A v 7 mag box
ℳ s from a Gaussian fit
a 1 = 0.0520.016
a 2 = 0.2200.21
J
0
0 10
ing the high-dynamic-range dust extinction maps. The
result indicates the correlation coefficient b 0.23 (3– σ
interval [0.02, 0.81]). While the detection is tentative,
the work demonstrates the feasibility of the technique
to probe the relation when applied for a larger sample
of IRDCs.
In the future: improving the link between the
observations and numerical modeling
I
B
A
D
IRDCs
nearby clouds
Brunt et al. (2010)
Padoan et al. (1997)
The dust extinction mapping techniques developed and
used by scientists in MPIA offer highly-capable observational
tools for constraining current models of star
formation through accurate measurements of the mass
distribution in molecular clouds. The structural characteristics
derived from those data can be connected to
predictions given by numerical simulations, and from
therein, they can constrain the physics of the molecular
cloud evolution. The current times are particularly interesting
in this respect, because the state-of-the-art simulations
are now starting to reach the level in which they
can include all physical processes that are believed to
play a role in shaping the ISM. Consequently, it will be
of great interest and urgency to the community to link
these simulations with observational data in a physically
meaningful way. The particular questions to consider
under this topic are: What structural parameters are the
most meaningful probes of the underlying physical processes?
What are the requirements of different observational
techniques when connecting them with simulations?
What is the framework, or a set of practices, that
can be commonly used in transforming simulated structural
parameters to an observational plane? Establishing
this framework is the first step in the work required to
take the advantage of the most recent numerical and observational
studies of the ISM structure.
In the context of the paradigm of pressure confinement
in molecular clouds, an impending question is
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ℳ s
G
F
C
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Credit: J. Kainulainen