From particles to orbits: precise dark matter density profiles using dynamical information

TL;DR

This paper presents a novel method to derive dark matter halo density profiles by orbit-averaging particle data, reducing noise and extending the inner cusp profiles closer to the center, with minimal computational cost.

Contribution

The authors introduce a dynamical orbit-based approach to accurately determine dark matter density profiles, improving resolution at small radii compared to traditional binning methods.

Findings

Dynamical profiles agree with binned profiles but have less noise.

Inner cusps extend inward to the softening radius, matching high-resolution simulations.

Method works well for halos in virial equilibrium despite asymmetries.

Abstract

We introduce a new method to calculate dark matter halo density profiles from simulations. Each particle is 'smeared' over its orbit to obtain a dynamical profile that is averaged over a dynamical time, in contrast to the traditional approach of binning particles based on their instantaneous positions. The dynamical and binned profiles are in good agreement, with the dynamical approach showing a significant reduction in Poisson noise in the innermost regions. We find that the inner cusps of the new dynamical profiles continue inward all the way to the softening radius, reproducing the central density profile of higher resolution simulations within the 95$\%$ confidence intervals, for haloes in virial equilibrium. Folding in dynamical information thus provides a new approach to improve the precision of dark matter density profiles at small radii, for minimal computational cost. Our technique makes two key assumptions: that the halo is in equilibrium (phase mixed), and that the potential is spherically symmetric. We discuss why the method is successful despite strong violations of spherical symmetry in the centres of haloes, and explore how substructures disturb equilibrium at large radii.