Quantifying and controlling biases in dark matter halo concentration estimates

TL;DR

This paper evaluates biases in dark matter halo concentration estimates from N-body simulations, introduces a new bias-reducing algorithm based on mass profiles, and discusses implications for understanding halo structures.

Contribution

The paper presents a novel algorithm using integrated mass profiles that significantly reduces bias in concentration estimates at low particle numbers.

Findings

Biases in traditional methods can reach 15-20% at 200 particles.

The new mass profile method reduces bias to less than 3% for 200-500 particles.

Mass-concentration relation may be shallower when biases are properly accounted for.

Abstract

We use bootstrapping to estimate the bias of concentration estimates on N-body dark matter halos as a function of particle number. We find that algorithms based on the maximum radial velocity and radial particle binning tend to overestimate the concentration by 15%-20% for halos sampled with 200 particles and by 7% - 10% for halos sampled with 500 particles. To control this bias at low particle numbers we propose a new algorithm that estimates halo concentrations based on the integrated mass profile. The method uses the full particle information without any binning, making it reliable in cases when low numerical resolution becomes a limitation for other methods. This method reduces the bias to less than 3% for halos sampled with 200-500 particles. The velocity and density methods have to use halos with at least 4000 particles in order to keep the biases down to the same low level. We also show that the mass-concentration relationship could be shallower than expected once the biases of the different concentration measurements are taken into account. These results show that bootstrapping and the concentration estimates based on the integrated mass profile are valuable tools to probe the internal structure of dark matter halos in numerical simulations.