论文标题

哥斯达黎加:冷水探测器算法。在离散示踪剂的相空间中搜索运动运动子结构

COSTA: the COld STream finder Algorithm. Searching for kinematical substructures in the phase space of discrete tracers

论文作者

Gatto, M., Napolitano, N. R., Spiniello, C., Longo, G., Paolillo, M.

论文摘要

上下文:我们提出了Costa(Cold Stream Finder算法),这是一种新型算法,可在行星星云(PNE)(PNE)(PNE)和球形簇(GCS)中寻找冷动力学子结构(GCS)的新型算法(GCS)。目的:Costa旨在检测小尺寸的低速色散流,就像矮星系与更大质量星系的光环(包括位于丰富星系簇中央区域的光环)中所产生的那样。方法:Costa是基于一个深层的朋友程序,该过程使用迭代(n)sigma夹在定义的(k)邻居粒子上的迭代(n)sigma覆盖(n)sigma,隔离了n个颗粒的n颗粒组(10 kms至$ \ sim $ 100 kms)。该算法具有三个参数(K-n-n),加上速度分散截止,该速度定义了流的“冷”,这些速度是使用蒙特卡洛实现的样本在检查中设置的。结果:在本文中,我们显示了Costa从公开可用的流体动力学模拟中恢复具有不同大小和测量误差的离散运动示踪剂模拟数据集的模拟流的能力。我们还显示了最佳的算法设置,用于在Fornax群集的核心中进行现实的流案例,以将Costa的未来应用于PNE和GC的真实种群。结论:可以将Costa推广到在有限的离散示踪剂样本样本中找到小的子结构的所有问题,前提是该算法是对现实的模拟观测值进行培训的,从而重现了考试中的特定数据集。

Context: We present COSTA (COld STream finder Algorithm), a novel algorithm to search for cold kinematical substructures in the phase space of planetary nebulae (PNe) and globular clusters (GCs) in the halo of massive galaxies and intracluster regions. Aims: COSTA aims at detecting small sized, low velocity dispersion streams, as the ones produced in recent interactions of dwarf galaxies with the halo of more massive galaxies, including the ones sitting in the central region of rich galaxy clusters. Methods: COSTA is based on a deep friend-of-friend procedure that isolates groups of N particles with small velocity dispersion (between 10 kms and $\sim$ 100 kms), using an iterative (n) sigma-clipping over a defined number of (k) neighbor particles. The algorithm has three parameters (k-n-N), plus a velocity dispersion cut-off, which defines the "coldness" of the stream, that are set using Montecarlo realizations of the sample under exam. Results: In this paper, we show the ability of COSTA to recover simulated streams on mock data-sets of discrete kinematical tracers with different sizes and measurement errors, from publicly available hydrodynamical simulations. We also show the best algorithm set-up for a realistic case of stream finding in the core of the Fornax cluster, for future applications of COSTA to real populations of PNe and GCs. Conclusions: COSTA can be generalized to all problems of finding small substructures in the phase space of a limited sample of discrete tracers, provided that the algorithm is trained on realistic mock observations reproducing the specific dataset under exam.

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