Peaks above the Maxwellian Sea: A New Approach to Finding Substructure in N-Body Haloes

TL;DR

This paper introduces a novel, computationally efficient algorithm for detecting substructures like subhaloes and tidal streams within dark matter haloes in N-body simulations by analyzing local velocity distributions.

Contribution

The paper presents a new velocity-based substructure detection algorithm that does not require full phase-space searches, improving efficiency and sensitivity to various substructure types.

Findings

Successfully identifies subhaloes and tidal streams

Recovers streams without significant overdensity

Operates efficiently without full phase-space search

Abstract

We describe a new algorithm for finding substructures within dark matter haloes from N-body simulations. The algorithm relies upon the fact that dynamically distinct substructures in a halo will have a {\em local} velocity distribution that differs significantly from the mean, i.e. smooth background halo. We characterize the large-scale mean field using a coarsely grained cell-based approach, while a kernel smoothing process is used to determined the local velocity distribution. Comparing the ratio of these two estimates allows us to identify particles which are strongly cluster in velocity space relative to the background and thus resident in substructure. From this population of outliers, groups are identified using a Friends-of-Friends-like approach. False positives are rejected using Poisson noise arguments. This approach does not require a search of the full phase-space structure of a halo, a non-trivial task, and is thus computationally advantageous. We apply our algorithm to several test cases and show that it identifies not only subhaloes, bound overdensities in phase-space, but can recover tidal streams with a high purity. Our method can even find streams which do not appear significantly overdense in either physical or phase-space.