Learning the Relationship between Galaxies Spectra and their Star
  Formation Histories using Convolutional Neural Networks and Cosmological
  Simulations
  We present a new method for inferring galaxy star formation histories (SFH)
using machine learning methods coupled with two cosmological hydrodynamic
simulations. We train Convolutional Neural Networks to learn the relationship
between synthetic galaxy spectra and high resolution SFHs from the EAGLE and
Illustris models. To evaluate our SFH reconstruction we use Symmetric Mean
Absolute Percentage Error (SMAPE), which acts as a true percentage error in the
low-error regime. On dust-attenuated spectra we achieve high test accuracy
(median SMAPE $= 10.5\%$). Including the effects of simulated observational
noise increases the error ($12.5\%$), however this is alleviated by including
multiple realisations of the noise, which increases the training set size and
reduces overfitting ($10.9\%$). We also make estimates for the observational
and modelling errors. To further evaluate the generalisation properties we
apply models trained on one simulation to spectra from the other, which leads
to only a small increase in the error (median SMAPE $\sim 15\%$). We apply each
trained model to SDSS DR7 spectra, and find smoother histories than in the
VESPA catalogue. This new approach complements the results of existing SED
fitting techniques, providing star formation histories directly motivated by
the results of the latest cosmological simulations.
