A simple method to convert sink particles into stars
Hydrodynamical simulations of star formation often do not possess the dynamic range needed to fully resolve the build-up of individual stars and star clusters, and thus have to resort to sub-grid models. A popular way to do this is by introducing Lagrangian sink particles, which replace contracting...
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Published in | Monthly notices of the Royal Astronomical Society Vol. 466; no. 1; p. 407 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
London
Oxford University Press
01.04.2017
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Subjects | |
Online Access | Get full text |
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Summary: | Hydrodynamical simulations of star formation often do not possess the dynamic range needed to fully resolve the build-up of individual stars and star clusters, and thus have to resort to sub-grid models. A popular way to do this is by introducing Lagrangian sink particles, which replace contracting high-density regions at the point where the resolution limit is reached. A common problem then is how to assign fundamental stellar properties to sink particles, such as the distribution of stellar masses. We present a new and simple statistical method to assign stellar contents to sink particles. Once the stellar content is specified, it can be used to determine a sink particle's radiative output, supernovae rate or other feedback parameters that may be required in the calculations. Advantages of our method are: (i) it is simple to implement; (ii) it guarantees that the obtained stellar populations are good samples of the initial mass function; (iii) it can easily deal with infalling mass accreted at later times; and (iv) it does not put restrictions on the sink particles' masses in order to be used. The method works very well for sink particles that represent large star clusters and for which the stellar mass function is well sampled, but can also handle the transition to sink particles that represent a small number of stars. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0035-8711 1365-2966 |
DOI: | 10.1093/mnras/stw3205 |