Classification algorithms applied to structure formation simulations

TL;DR

This paper demonstrates that random-forest classifiers can predict the formation of dark matter halos from initial conditions in cosmological simulations, potentially reducing computational costs.

Contribution

It introduces the use of random forests to predict halo formation from initial density fields, offering a new approach to analyze cosmological simulations efficiently.

Findings

Random forests accurately predict halo formation.

The method reduces the need for full simulations.

Potential to explore dark matter models more efficiently.

Abstract

Throughout cosmological simulations, the properties of the matter density field in the initial conditions have a decisive impact on the features of the structures formed today. In this paper we use a random-forest classification algorithm to infer whether or not dark matter particles, traced back to the initial conditions, would end up in dark matter halos whose masses are above some threshold. This problem might be posed as a binary classification task, where the initial conditions of the matter density field are mapped into classification labels provided by a halo finder program. Our results show that random forests are effective tools to predict the output of cosmological simulations without running the full process. These techniques might be used in the future to decrease the computational time and to explore more efficiently the effect of different dark matter/dark energy candidates on the formation of cosmological structures.