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

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number34
JournalThe Astrophysical Journal
Publication statusPublished - 1 Nov 2022


  • Dark matter distribution
  • Galaxy dark matter halos
  • Spatial point processes
  • Mixture model
  • 356
  • 1880
  • 1915
  • 1932
  • Astrophysics - Astrophysics of Galaxies


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