Machine Learning the Fates of Dark Matter Subhalos: A Fuzzy Crystal Ball

Abstract

The evolution of a dark matter halo in a dark matter only simulation is governed purely byNewtonian gravity, making a clean testbed to determine what halo properties drive its fate.Using machine learning, we predict the survival, mass loss, final position, and merging time of subhalos within a cosmological N-body simulation, focusing on what instantaneous initial features of the halo, interaction, and environment matter most. Survival is well predicted, with our model achieving 96.5% accuracy using only 3 model inputs from the initial interaction.However, the mass loss, final location, and merging times are much more stochastic processes, with significant margins of error between the true and predicted quantities for much of our sample. The redshift, impact angle, relative velocity, and the masses of the host and subhalo are the only relevant initial inputs for determining subhalo evolution. In general, subhalos that enter their hosts at a mid-range of redshifts (typically z = 0.67-0.43) are the most challenging to make predictions for, across all of our final outcomes. Subhalo orbits that come in more perpendicular to the host are also easier to predict, except for in the case of predicting disruption, where the opposite appears to be true. We conclude that the detailed evolution of individual subhalos within N-body simulations is quite difficult to predict, pointing to a stochasticity in the merging process. We discuss implications for both simulations and observations