Gravitational Collapse and Fragmentation of Molecular Cloud Cores with GADGET-2

Details Gravitational Collapse and Fragmentation of Molecular Cloud Cores with GADGET-2 Gravitational Collapse and Fragmentation of Molecular Cloud Cores with GADGET-2

Sep 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007apj...666..290a&link_type=abstract

The Astrophysical Journal, Volume 666, Issue 1, pp. 290-308.

Astronomy and Astrophysics

Astronomy

11

Stars: Binaries: General, Hydrodynamics, Ism: Clouds, Methods: Numerical, Stars: Formation

Scientific paper

The collapse and fragmentation of molecular cloud cores is examined numerically with unprecedentedly high spatial resolutions, using the publicly released code GADGET-2. As templates for the model clouds we use the ``standard isothermal test case'' in the variant calculated by Burkert & Bodenheimer in 1993 and the centrally condensed, Gaussian cloud advanced by Boss in 1991. A barotropic equation of state is used to mimic the nonisothermal collapse. We investigate both the sensitivity of fragmentation to thermal retardation and the level of resolution needed by smoothed particle hydrodynamics (SPH) to achieve convergence to existing Jeans-resolved, finite-difference (FD) calculations. We find that working with 0.6-1.2 million particles, acceptably good convergence is achieved for the standard test model. In contrast, convergent results for the Gaussian-cloud model are achieved using from 5 to 10 million particles. If the isothermal collapse is prolonged to unrealistically high densities, the outcome of collapse for the Gaussian cloud is a central adiabatic core surrounded by dense trailing spiral arms, which in turn may fragment in the late evolution. If, on the other hand, the barotropic equation of state is adjusted to mimic the rise of temperature predicted by radiative transfer calculations, the outcome of collapse is a protostellar binary core. At least, during the early phases of collapse leading to formation of the first protostellar core, thermal retardation not only favors fragmentation but also results in an increased number of fragments, for the Gaussian cloud.

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