# DASH: a library of dynamical subhalo evolution

## TL;DR

This paper introduces a library of high-resolution N-body simulations for the tidal evolution of dark matter subhaloes, providing a tool to calibrate semi-analytical models and address numerical artefacts in cosmological simulations.

## Contribution

It presents a comprehensive library of idealized subhalo evolution simulations and a pre-trained machine learning model for efficient interpolation of subhalo mass evolution.

## Key findings

- Simulation library covers realistic orbital and concentration parameters.
- Pre-trained model interpolates simulation data at 0.1 dex accuracy.
- Library aids in improving semi-analytical models of subhalo evolution.

## Abstract

The abundance and demographics of dark matter substructure is 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 setup 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 publicly available a pre-trained non-parametric model of the subhalo mass evolution based on random forest regression. 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.