The structure of molecular clouds and the universality of the clump mass function
arXiv:0809.2702 · doi:10.1111/j.1365-2966.2008.13970.x
Abstract
Using an SPH simulation of a star-forming region in a molecular cloud, we show that the emergence of a clump mass function (CMF) resembling the stellar initial mass function (IMF) is a ubiquitous feature of molecular cloud structure, but caution against its over-interpretation. We employ three different techniques to extract the clumps in this study. In the first two, we interpolate the SPH particle data to 2 and 3 dimensional grids before performing the clump-find, using position-position (PP) and position-position-velocity (PPV) information respectively. In the last technique, the clump-finding is performed on the SPH data directly, making use of the full 3 dimensional position information. Although the CMF is typically similar to that observed in regions of nearby star formation, the individual clumps and their masses are found to be unreliable since they depend strongly on the parameters and the method of the clump-finding. In particular we find that the resolution and orientation of the data make a significant difference to the resulting properties of the identified clumps in the PP and PPV cases. We conclude that making comparisons between a CMF and the stellar IMF should be done with caution, since the definition of a clump boundary, and hence the number of clumps and their properties, are arbitrary in the extraction method. This is especially true if molecular clouds are truly scale free.
11 pages, 10 figures, accepted for publication in MNRAS
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- The IMF and multiplicity of stars from gravity, turbulence, magnetic fields, radiation and outflow feedback
- Does turbulence determine the initial mass function?
- Implementation of stellar heating feedback in simulations of star cluster formation: effects on the initial mass function
- Connecting the Dots: Analyzing Synthetic Observations of Star-Forming Clumps in Molecular Clouds
- A Guide to Comparisons of Star Formation Simulations with Observations