SIMBAD references

We build upon Ordering Points To Identify the 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 visualization 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 localized information such as particle kinematics and stellar metallicity into its distance metric.