DASH: a library of dynamical subhalo evolution. Issue 1 (7th February 2019)

• Record Type: Journal Article
• Authors: Ogiya, Go; van den Bosch, Frank C; Hahn, Oliver; Green, Sheridan B; Miller, Tim B; Burkert, Andreas
• Is Part Of: Monthly notices of the Royal Astronomical Society. Volume 485:Issue 1(2019)
• Page Start: 189
• Page End: 202
• Publication Date: 2019-02-07
• Subjects: methods: numerical -- galaxies: haloes -- dark matter; Astronomy -- Periodicals; Periodicals; 520.5
• DOI: 10.1093/mnras/stz375
• Languages: English
• ISSNs: 0035-8711

Abstract

Abstract: The abundance and demographics of dark matter substructure are important for many areas in astrophysics and cosmological N -body simulations have been the primary tool used to investigate them. However, it has recently become clear that the simulations are subject to numerical artefacts, which hampers a proper treatment of the tidal evolution of subhaloes. Unfortunately, no analytical models that accurately describe subhalo evolution exist either. We therefore present a library of idealized, high-resolution N -body simulations of the tidal evolution of individual subhaloes that can be used to calibrate semi-analytical models and to complement cosmological simulations. The simulations focus on minor mergers, i.e. the mass of the subhalo is much smaller than that of the host halo, such that the impact of dynamical friction is negligible. This set-up allows the adoption of a fixed analytical potential for modelling the host halo. The dynamical evolution of subhaloes is followed with N -body computations. In the library, four parameters, two of which characterize the subhalo orbit with respect to the host halo, and the two concentrations of the host- and subhalo, are varied over the ranges encountered in cosmological simulations. We show several representative examples from the library that illustrate the evolution of the subhalo mass and velocity dispersion profiles. Additionally, we make a pre-trained non-parametric model of the subhalo mass evolution based on random forest regression publicly available. This model is able to interpolate the simulation data at the 0.1 dex level and provides efficient access to the data for further use in modelling. 1