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

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 mi...

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Bibliographic Details
Published in:The Astrophysical journal 2022-11, Vol.939 (1), p.34
Main Authors: Hurtado-Gil, Lluís, Kuhn, Michael A., Arnalte-Mur, Pablo, Feigelson, Eric D., Martínez, Vicent
Format: Article
Language:English
Subjects:
Algorithms
Astrophysics
Dark matter
Dark matter distribution
Galactic halos
Galaxy dark matter halos
Halos
Machine learning
Mixture model
Mixtures
Modelling
Probabilistic models
Simulation
Spatial point processes
Statistical methods
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Summary: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 .
ISSN:0004-637X
1538-4357
DOI:10.3847/1538-4357/ac88d4