Papers › DarkMix: Mixture Models for the Detection and Characterization of Dark Matter Halos
DarkMix: Mixture Models for the Detection and Characterization of Dark Matter Halos
Lluís Hurtado-Gil, Michael A. Kuhn, Pablo Arnalte-Mur, Eric D. Feigelson, Vicent Martínez
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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.
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