Convergence Tests of Self-Interacting Dark Matter Simulations
Charlie Mace, Zhichao Carton Zeng, Annika H. G. Peter, Xiaolong Du,, Shengqi Yang, Andrew Benson, Mark Vogelsberger

TL;DR
This study performs convergence tests on self-interacting dark matter simulations to identify numerical errors and optimal parameters, ensuring reliable modeling of core-collapse in dark matter halos.
Contribution
It provides a detailed analysis of how simulation parameters affect the accuracy of SIDM halo core-collapse modeling, highlighting issues related to resolution, timestep, and softening length.
Findings
Halos with fewer than 10^5 particles show significant noise and outliers.
Low-resolution simulations can have collapse time variations up to 20%.
Simulations beyond the age of the Universe are sensitive to timestep size.
Abstract
Self-interacting dark matter (SIDM) theory predicts that dark matter halos experience core-collapse, a process where the halo's inner region rapidly increases in density and decreases in size. The N-body simulations used to study this process can suffer from numerical errors when simulation parameters are selected incorrectly. Optimal choices for simulation parameters are well studied for cold dark matter (CDM), but are not deeply understood when self-interactions are included. In order to perform reliable N-body simulations and model core-collapse accurately we must understand the potential numerical errors, how to diagnose them, and what parameter selections must be made to reduce them. We use the \texttt{Arepo} N-body code to perform convergence tests of core-collapsing SIDM halos across a range of halo concentrations and SIDM cross-sections, and quantify potential numerical issues…
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Taxonomy
TopicsDark Matter and Cosmic Phenomena · Functional Brain Connectivity Studies · Atomic and Subatomic Physics Research
