DarkMix: Mixture Models for the Detection and Characterization of Dark Matter Halos

TL;DR

DarkMix introduces a mixture model approach using the Einasto profile to accurately detect and characterize dark matter halos in simulations, improving upon nonparametric methods by providing detailed structural parameters.

Contribution

The paper presents a novel mixture model method for identifying and analyzing dark matter halos, incorporating the Einasto profile within the R environment, enhancing accuracy over existing nonparametric techniques.

Findings

Successfully modeled halos in the Bolshoi simulation

Provided detailed estimates of halo location, size, shape, and mass

Code implementation available for reproducibility

Abstract

Dark matter simulations require statistical techniques to properly identify and classify their halos and structures. Nonparametric solutions provide catalogs of these structures but lack the additional learning of a model-based algorithm and might misclassify particles in merging situations. With mixture models, we can simultaneously fit multiple density profiles to the halos that are found in a dark matter simulation. In this work, we use the Einasto profile (Einasto 1965, 1968, 1969) to model the halos found in a sample of the Bolshoi simulation (Klypin et al. 2011), and we obtain their location, size, shape and mass. Our code is implemented in the R statistical software environment and can be accessed on https://github.com/LluisHGil/darkmix.