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

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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 to model the halos found in a sample of the Bolshoi simulation, 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.
Funding Information
  • Ministerio de Ciencia, Innovación y Universidades (PID2019-109592GB-100/AEI/10.13039/501100011033)
  • GVA ∣ Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana (Prometeo/2020/085)

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