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arXiv · 2208.04194

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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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.

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Lluís Hurtado-Gil, Michael A. Kuhn, Pablo Arnalte-Mur, Eric D. Feigelson, Vicent Martínez. 2022-08-08. DarkMix: Mixture Models for the Detection and Characterization of Dark Matter Halos. https://doi.org/10.3847/1538-4357%2Fac88d4

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