The specific angular momentum profile of cores in colliding flow simulations
Michael Weis
The specific angular momentum profile j(r) of cores is important for constraining models of star formation, especially with regard to the formation of a circumstellar disk.
We carry out colliding flow simulations to model the formation of molecular clouds and their substructures (Joshi et al., 2019; Weis et al., 2022). The simulations use adaptive mesh refinement with a peak resolution of 1600 AU and include self-gravity and chemistry. We abstain from using sink particles in this particular set of simulations to avoid distorting the density structure of the molecular cloud substructures.
The simulations begin to form molecular clouds after a few Myr. We detect 3D substructures in those clouds in form of clumps and cores, where the cores are detected by finding interconnected molecular structures above a 3D visual extinction of 8 mag against the ambience. The first cores are detected at around 14 Myr simulation time and numerous cores can be observed at 20 Myr.
We then analyse the properties of the 3D-cores at 20 Myr. Figure 1 shows the specific angular momentum radial profiles of our cores, where each line represents the radial profile of one core. The line colour represents the core size, as given by (〈r2〉m)1/2. The dash-dotted line shows the best-fit power-law given by Pineda et al. (2019) for their study of three sources, the dashed line shows the profile as proposed by Ohashi et al. (1997) & Belloche (2013).
We note following things:
• For our cores, the radial dependency of the specific angular momentum, j(r),
grows faster in the inner radii than in the outer radii of our cores. (Note that
our cores in general are not spherical, thus the outer radii might not be volume
filling.)
• For a given radius r, the values of j(r) for our cores vary over more than one
order of magnitude.
• Aforementioned variation is correlated to the size of the cores.
• The overall slope exponent of j(r) of our cores is similar to that found in
observations (Pineda et al., 2019).