The Hierarchical Structure of Galactic Haloes: Classification and characterisation with Halo-OPTICS

TL;DR

This paper introduces Halo-OPTICS, a hierarchical clustering algorithm for identifying and characterizing galaxy haloes from 3D spatial data, demonstrating its effectiveness on simulated Milky Way-like galaxies.

Contribution

Halo-OPTICS extends the OPTICS algorithm to automatically detect hierarchical structures in galactic haloes without kinematic data, showing strong agreement with existing halo finders.

Findings

Halo-OPTICS accurately identifies galaxy halo structures.

It shows excellent agreement with VELOCIraptor in structure detection.

The method can be improved with additional data like kinematics.

Abstract

We build upon Ordering Points To Identify Clustering Structure (OPTICS), a hierarchical clustering algorithm well-known to be a robust data-miner, in order to produce Halo-OPTICS, an algorithm designed for the automatic detection and extraction of all meaningful clusters between any two arbitrary sizes. We then apply Halo-OPTICS to the 3D spatial positions of halo particles within four separate synthetic Milky Way type galaxies, classifying the stellar and dark matter structural hierarchies. Through visualisation of the Halo-OPTICS output, we compare its structure identification to the state-of-the-art galaxy/(sub)halo finder VELOCIraptor, finding excellent agreement even though Halo-OPTICS does not consider kinematic information in this current implementation. We conclude that Halo-OPTICS is a robust hierarchical halo finder, although its determination of lower spatial-density features such as the tails of streams could be improved with the inclusion of extra localised information such as particle kinematics and stellar metallicity into its distance metric.