Modelling the galaxy-halo connection with semi-recurrent neural networks
  We present an artificial neural network design in which past and present-day
properties of dark matter halos and their local environment are used to predict
time-resolved star formation histories and stellar metallicity histories of
central and satellite galaxies. Using data from the IllustrisTNG simulations,
we train a TensorFlow-based neural network with two inputs: a standard layer
with static properties of the dark matter halo, such as halo mass and starting
time; and a recurrent layer with variables such as overdensity and halo mass
accretion rate, evaluated at multiple time steps from 0 < z < 20. This
constitutes a model which can be applied to data from a pure dark matter
simulation, emulating galaxy formation histories in much greater volumes than
what is currently feasible with cosmohydrodynamical simulations. The model
successfully reproduces key features of the galaxy halo connection, such as the
stellar-to-halo mass relation, downsizing, and colour bimodality. With temporal
measures of mass growth, subhalo infall and dark matter concentration, we
identify mass accretion history as crucial in determining the geometry of the
star formation history and trends with halo mass such as downsizing, while
environmental variables are important indicators of chemical enrichment. We use
these outputs to compute optical spectral energy distributions, and find that
they are well matched to the equivalent results in IllustrisTNG, recovering
observational statistics such as colour bimodality and mass-magnitude diagrams.
