Abstract
AbstractWe introduce phew (Parallel HiErarchical Watershed), a new segmentation algorithm to detect structures in astrophysical fluid simulations, and its implementation into the adaptive mesh refinement (AMR) code ramses. phew works on the density field defined on the adaptive mesh, and can thus be used on the gas density or the dark matter density after a projection of the particles onto the grid. The algorithm is based on a ‘watershed’ segmentation of the computational volume into dense regions, followed by a merging of the segmented patches based on the saddle point topology of the density field. phew is capable of automatically detecting connected regions above the adopted density threshold, as well as the entire set of substructures within. Our algorithm is fully parallel and uses the MPI library. We describe in great detail the parallel algorithm and perform a scaling experiment which proves the capability of phew to run efficiently on massively parallel systems. Future work will add a particle unbinding procedure and the calculation of halo properties onto our segmentation algorithm, thus expanding the scope of phew to genuine halo finding.
Highlights
Over the last decades, computer simulations have become an indispensable tool for studying the formation of structure on all scales in our universe
The aim of this paper is to present a new structure finding algorithm that: ( ) can be applied to any density field defined on an adaptive grid, ( ) is capable of detecting substructure, ( ) is parallelized using the MPI library on distributed memory systems, and ( ) is fast enough to be run at every time step of a simulation without significantly slowing down the calculation
5 Conclusions We have presented phew, a new structure finding algorithm and its MPI parallel implementation into the adaptive mesh refinement (AMR) code ramses. phew finds density peaks and their associated regions in a D density field by performing a watershed segmentation
Summary
Computer simulations have become an indispensable tool for studying the formation of structure on all scales in our universe. The common feature of those simulations is the clustering of matter due to self gravity. This clustering is of fractal nature in the sense that - as long as gravity is the dominant force - aggregations of matter turn out to have internal substructures, which are themselves gravitational bound, and may even contain subsubstructures. A crucial tasks in the analysis of simulations is the identification of overdense regions and, ideally, their entire hierarchy of substructure. First algorithms to perform this task have been invented in the very early days of computer simulations in Astronomy and Astrophysics. A halo finder based on spherical overdensities (SO) was described already four decades ago Bleuler et al Computational Astrophysics and Cosmology (2015) 2:5
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