| Resumen |
The properties of the matter density field in the initial conditions have a decisive impact on the features of the large-scale structure of the Universe as observed today. These need to be studied via N-body simulations, which are imperative to analyze high density collapsed regions into dark matter halos. In this paper, we train machine learning algorithms with information from N-body simulations to infer two properties: dark matter particle halo classification that leads to halo formation prediction with the characteristics of the matter density field traced back to the initial conditions, and dark matter halo formation by calculating the halo mass function, which offers the number density of dark matter halos with a given threshold. We map the initial conditions of the density field into classification labels of dark matter halo structures. The halo mass function of the simulations is calculated and reconstructed with theoretical methods as well as our trained algorithms. We test several machine learning techniques where we could find that the random forest and neural networks proved to be the better performing tools to classify dark matter particles in cosmological simulations. We also show that, by using only a few data points, we can effectively train the algorithms to reconstruct the halo mass function in a model-independent way, giving us a highly accurate fitting function that aligns well with both simulation and theoretical results. © 2023 American Physical Society.
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