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Towards a more realistic sink particle algorithm for the RAMSES code
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Towards a more realistic sink particle algorithm for the RAMSES code
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We present a new sink particle algorithm developed for the Adaptive Mesh Refinement code RAMSES. Our main addition is the use of a clump finder to identify density peaks and their associated regions (the peak patches). This allows us to unambiguously define a discrete set of dense molecular cores as potential sites for sink particle formation. Furthermore, we develop a new scheme to decide if the gas in which a sink could potentially form, is indeed gravitationally bound and rapidly collapsing. This is achieved using a general integral form of the virial theorem, where we use the curvature in the gravitational potential to correctly account for the background potential. We detail all the necessary steps to follow the evolution of sink particles in turbulent molecular cloud simulations, such as sink production, their trajectory integration, sink merging and finally the gas accretion rate onto an existing sink. We compare our new recipe for sink formation to other popular implementations. Statistical properties such as the sink mass function, the average sink mass and the sink multiplicity function are used to evaluate the impact that our new scheme has on accurately predicting fundamental quantities such as the stellar initial mass function or the stellar multiplicity function.
Forward citations
Cited by 2 Pith papers
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Virial-based extraction of structures in numerical simulations: The vibes tool
Vibes is a new algorithm that extracts physically motivated core structures from numerical star formation simulations by applying the virial theorem iteratively around density peaks to determine boundaries from energy...
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Virial-based extraction of structures in numerical simulations: The vibes tool
Vibes extracts cores in simulations using the virial theorem to define boundaries, yielding more stable and physically motivated structures than density-threshold methods like hop and dendrogram.
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