HIKER: a halo-finding method based on kernel-shift algorithm

We introduce a new halo/subhalo finder, HIKER (a Halo fInder based on KERnel-shift algorithm), which takes advantage of a machine learning method -- the mean-shift algorithm combined with the Plummer kernel function, to effectively locate density peaks corresponding to halos/subhalos in density fiel...

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Veröffentlicht in:arXiv.org 2019-10
Hauptverfasser: Sun, Shuangpeng, Liao, Shihong, Guo, Qi, Wang, Qiao, Gao, Liang
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Gao, Liang
description We introduce a new halo/subhalo finder, HIKER (a Halo fInder based on KERnel-shift algorithm), which takes advantage of a machine learning method -- the mean-shift algorithm combined with the Plummer kernel function, to effectively locate density peaks corresponding to halos/subhalos in density field. Based on these density peaks, dark matter halos are identified as spherical overdensity structures, and subhalos are bound substructures with boundaries at their tidal radius. By testing HIKER code with mock halos, we show that HIKER performs excellently in recovering input halo properties. Especially, HIKER has higher accuracy in locating halo/subhalo centres than most halo finders. With cosmological simulations, we further show that HIKER reproduces the abundance of dark matter halos and subhalos quite accurately, and the HIKER halo/subhalo mass functions and \(V_{max}\) functions are in good agreement with two widely used halo finders, SUBFIND and AHF.
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subjects Algorithms
Computer simulation
Dark matter
Density
Galactic halos
Kernel functions
Machine learning
Physics - Cosmology and Nongalactic Astrophysics
Substructures
title HIKER: a halo-finding method based on kernel-shift algorithm
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