Subhaloes going Notts: the subhalo-finder comparison project

Abstract

We present a detailed comparison of the substructure properties of a single Milky Way sized dark matter halo from the Aquarius suite at five different resolutions, as identified by a variety of different (sub)halo finders for simulations of cosmic structure formation. These finders span a wide range of techniques and methodologies to extract and quantify substructures within a larger non‐homogeneous background density (e.g. a host halo). This includes real‐space‐, phase‐space‐, velocity‐space‐ and time‐space‐based finders, as well as finders employing a Voronoi tessellation, Friends‐of‐Friends techniques or refined meshes as the starting point for locating substructure. A common post‐processing pipeline was used to uniformly analyse the particle lists provided by each finder. We extract quantitative and comparable measures for the subhaloes, primarily focusing on mass and the peak of the rotation curve for this particular study. We find that all of the finders agree extremely well in the presence and location of substructure and even for properties relating to the inner part of the subhalo (e.g. the maximum value of the rotation curve). For properties that rely on particles near the outer edge of the subhalo the agreement is at around the 20 per cent level. We find that the basic properties (mass and maximum circular velocity) of a subhalo can be reliably recovered if the subhalo contains more than 100 particles although its presence can be reliably inferred for a lower particle number limit of 20. We finally note that the logarithmic slope of the subhalo cumulative number count is remarkably consistent and <1 for all the finders that reached high resolution. If correct, this would indicate that the larger and more massive, respectively, substructures are the most dynamically interesting and that higher levels of the (sub)subhalo hierarchy become progressively less important.

Exploring the liminality: properties of haloes and subhaloes in borderlinef(R) gravity

“…With a single walk through all the snapshots, all the subhaloes formed from halo mergers can be identified in this way. Such a unique tracking algorithm enables HBT to largely avoid the resolution problem suffered by configuration space subhalo finders (Muldrew, Pearce & Power 2011;Han et al 2012b;Onions et al 2012). By construction, HBT also produces clean and self-consistent merger trees that naturally avoid subtle defects such as missing links and central-satellite swaps common to many other tree builders (Srisawat et al 2013;Avila et al 2014).…”

Milky Way mass constraints from the Galactic satellite gap

“…We use this quantity to characterize the halo population because the maximum velocity provides a robust measurement of subhalo size that is independent of the identification algorithm and definition of subhalo boundary (for details see Onions et al 2012). Moreover, since V max depends only on the mass distribution in the central parts of the object, it allows for a closer comparison with observations that typically probe only the inner regions of a halo where the galaxy resides.…”

Physical drivers of galaxies’ cold-gas content: exploring environmental and evolutionary effects with Dark Sage

“…Some difference in these values is expected, due to the nature of group finding versus subhalo finding for the observed galaxies and simulated data, respectively. In principle, subhalo finding for simulations is a more precise means of identifying satellites (although there is certainly some variation amongst codes -see Onions et al 2012;Knebe et al 2013;Behroozi et al 2015). Projection effects make it possible for true centrals to be observationally classified as satellites with a group finder, but the converse is unlikely (see, e.g., Campbell et al 2015).…”

“…These terms are most relevant for numerical simulations, where a central galaxy belongs to the most massive subhalo of a halo (see, e.g., Springel et al 2001;Onions et al 2012). All remaining subhaloes host satellite galaxies.…”