HIKER：一种基于核移位算法的晕轮发现方法
HIKER: a halo-finding method based on kernel-shift algorithm
摘要：
本文介绍了一种新的Halo/subhalo发现器HIKER（a Halo fInder based on KERnel-shift algorithm），它利用一种机器学习方法--均值漂移算法结合Plummer核函数，有效地定位密度场中对应于Halo/subhalo的密度峰。基于这些密度峰值，暗物质晕被确定为球形的过密度结构，而暗物质晕则是以潮汐半径为边界的束缚子结构。通过测试HIKER代码与模拟晕，我们表明，HIKER表现出色，在恢复输入晕属性。特别是，HIKER在定位晕/亚晕中心方面比大多数晕发现者具有更高的准确性。通过宇宙学模拟，我们进一步证明了HIKER相当准确地再现了暗物质晕和次晕的丰度，HIKER晕/次晕质量函数和$V_{max}$函数与两种广泛使用的晕发现器SUBFIND和AHF吻合得很好.
英文摘要：
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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