Multi-Epoch Machine Learning 1: Unravelling Nature vs Nurture for Galaxy
  Formation
  We present a novel machine learning method for predicting the baryonic
properties of dark matter only subhalos from N-body simulations. Our model is
built using the extremely randomized tree (ERT) algorithm and takes subhalo
properties over a wide range of redshifts as its input features. We train our
model using the IllustrisTNG simulations to predict blackhole mass, gas mass,
magnitudes, star formation rate, stellar mass, and metallicity. We compare the
results of our method with a baseline model from previous works, and against a
model that only considers the mass history of the subhalo. We find that our new
model significantly outperforms both of the other models. We then investigate
the predictive power of each input by looking at feature importance scores from
the ERT algorithm. We produce feature importance plots for each baryonic
property, and find that they differ significantly. We identify low redshifts as
being most important for predicting star formation rate and gas mass, with high
redshifts being most important for predicting stellar mass and metallicity, and
consider what this implies for nature vs nurture. We find that the physical
properties of galaxies investigated in this study are all driven by nurture and
not nature. The only property showing a somewhat stronger impact of nature is
the present-day star formation rate of galaxies. Finally we verify that the
feature importance plots are discovering physical patterns, and that the trends
shown are not an artefact of the ERT algorithm.
