  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.
