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Mathematisch-Naturwissenschaftliche FakultätAstrophysik I.Physik

Gravo turbulence or global hierarchical collapse?

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  • Figure 1: Projection plot of one molecular cloud obtained from the SILCC-Zoom Simulations. The maximum resolution for this particular simulation is 0.125 parsec.
  • Figure 2: Ratio of sum of kinetic (ΩKE), magnetic (ΩB), and thermal (ΩTE) to the self gravitating potential energy (ΩPE), against the size of different structures at a certain time. The colour bar shows the threshold or minimum density of a given structure. The red shaded region represents a factor of 2 around the line y=1. The filled and empty symbols represent molecular and atomic structures respectively. The plot shows that most of the structures are not bound by potential energy. Only a few are gravitationally bound and can go on to form stars.

Shashwata Ganguly

 

What mechanism drives a molecular cloud to collapse, form dense structures, and eventually even stars? How do we make star formation as inefficient as we actually observe it, with 99% of the gas never forming stars at all? There are two principal scenarios investigating this question. 

The gravo-turbulence scenario proposes that molecular clouds are not globally collapsing at all, but are held up by turbulent pressure. Local turbulent compressions cause overdensities. In turn these overdensities can become dense enough to collapse under gravity, and go on and form stars.

In contrast, the global hierarchical collapse scenario proposes that molecular clouds are undergoing gravitational collapse across every scale in the molecular cloud. This collapse proceeds anisotropically and hierarchically, and the cloud is dispersed by stellar feedback from massive stars before all of its mass can be converted into stars.

One way we can potentially distinguish between these scenarios is by carefully examining realistic simulations of molecular cloud formation. An example of such a simulated molecular cloud can be seen in Figure 1. This cloud is one of many that were formed self sufficiently in the SILCC-Zoom simulation of a stratified galactic disk (Seifried et al. 2017).

We identify structures within such clouds by applying a dendrogram algorithm on the three dimensional density distribution. This gives us a list of density structures, hierarchically arranged in a tree structure. By carefully analyzing the energetics of different structures within a cloud, we can comment on the dominant energy for molecular clouds at different length scales, and at different stages of molecular cloud evolution.

In Figure 2, one can see the ratio of kinetic, thermal, and magnetic energy to potential energy against the size of different structures. The kinetic energy is by far the dominant term of the three terms in the numerator. This shows that for the particular cloud shown in Figure 1, self gravity cannot prevail over the turbulent energy except in a few of the densest structures.