TL;DR
The paper introduces an enhanced, massively-parallel mass-Peak Patch algorithm that rapidly generates accurate dark matter halo catalogues for large cosmological volumes, validated against N-body simulations with significant computational efficiency.
Contribution
It presents a new parallel implementation of the mass-Peak Patch method that produces accurate halo catalogues without parameter fitting, vastly reducing computational resources needed.
Findings
Achieves over 3 orders of magnitude reduction in CPU time compared to N-body simulations.
Reproduces N-body results accurately across various redshifts and resolutions.
Efficiently generates large ensembles of halo catalogues for cosmological surveys.
Abstract
We present a detailed description and validation of our massively-parallel update to the mass-Peak Patch method, a fully predictive initial-space algorithm to quickly generate dark matter halo catalogues in very large cosmological volumes. We perform an extensive systematic comparison to a suite of N-body simulations covering a broad range of redshifts and simulation resolutions, and find that, without any parameter fitting, our method is able to generally reproduce N-body results while typically using over 3 orders of magnitude less CPU time, and a fraction of the memory cost. Instead of calculating the full non-linear gravitational collapse determined by an N-body simulation, the mass-Peak Patch method finds an overcomplete set of just-collapsed structures around a hierarchy of density-peak points by coarse-grained (homogeneous) ellipsoidal dynamics. A complete set of mass-peaks, or…
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