Annual Report 2011 Max Planck Institute for Astronomy

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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 20 ℳ s G F C 30 40 Credit: J. Kainulainen
whether the external pressure imposed to cloud structures results from turbulent pressure in the diffuse medium, or from gravitation-induced global contraction of the cloud. This question is not trivial to approach, because it is not clear how to observationally distinguish turbulent motions from global collapsing motions. Again, close interaction between numerical and observational communities are needed to develop tools that can approach the question. Given the above considerations, one important nearfuture goal of the MPIA scientists working on the ISM structure is to improve the interfacing between the observational work done at MPIA and the work of key numerical communities. This is being done by building and reinforcing collaborations with research groups performing numerical simulations and through projects that specifically not only provide predictions for various III.2 Formation of star-forming structures in the interstellar medium 55 physical parameters, but in fact make a direct connection between those parameters and observed quantities. Such work, together with the recent advances in the numerical and observational domains, can open a new era in our understanding of what controls star formation in the ISM. Jouni Kainulainen, Henrik Beuther, Thomas Henning In collaboration with: Institute of Theoretical Astrophysics, Heidelberg, Ecole Normale Suprieure de Lyon/France, University of Vienna, University of Florida, California Institute of Technology, Jet Propulsion Laboratory, MPI für Radioastronomie, Bonn
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Page 5: Contents Preface ..................
Page 8 and 9: 6 I. General Credit: MPIA / AMQ I.
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Annual Report 2011 Max Planck Institute for Astronomy

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