An Evolutionary Model for Collapsing Molecular Clouds and Their Star Formation Activity
Abstract
We present an idealized, semi-empirical model for the evolution of gravitationally contracting molecular clouds (MCs) and their star formation rate (SFR) and efficiency (SFE). The model assumes that the instantaneous SFR is given by the mass above a certain density threshold divided by its free-fall time. The instantaneous number of massive stars is computed assuming a Kroupa initial mass function. These stars feed back on the cloud through ionizing radiation, eroding it. The main controlling parameter of the evolution turns out to be the maximum cloud mass, M max. This allows us to compare various properties of the model clouds against their observational counterparts. A giant molecular cloud (GMC) model (M max ~ 105 M ⊙) adheres very well to the evolutionary scenario recently inferred by Kawamura et al. for GMCs in the Large Magellanic Cloud. A model cloud with M max ≈ 2000 M ⊙ evolves in the Kennicutt-Schmidt diagram, first passing through the locus of typical low-to-intermediate-mass star-forming clouds, and then moving toward the locus of high-mass star-forming ones over the course of ~10 Myr. Also, the stellar age histograms for this cloud a few Myr before its destruction agree very well with those observed in the ρ-Oph stellar association, whose parent cloud has a similar mass, and imply that the SFR of the clouds increases with time. Our model thus agrees well with various observed properties of star-forming MCs, suggesting that the scenario of gravitationally collapsing MCs, with their SFR regulated by stellar feedback, is entirely feasible and in agreement with key observed properties of MCs.
- Publication:
-
The Astrophysical Journal
- Pub Date:
- May 2012
- DOI:
- arXiv:
- arXiv:1105.4777
- Bibcode:
- 2012ApJ...751...77Z
- Keywords:
-
- evolution;
- ISM: clouds;
- stars: formation;
- Astrophysics - Astrophysics of Galaxies;
- 85A04;
- J.2
- E-Print:
- Version accepted for publication in ApJ. At referee's suggestion, includes comparison with numerical models in addition to comparison with observational data