Distinguishing standard and modified gravity cosmologies with machine
  learning
  We present a convolutional neural network to classify distinct cosmological
scenarios based on the statistically similar weak-lensing maps they generate.
Modified gravity (MG) models that include massive neutrinos can mimic the
standard concordance model ($\Lambda$CDM) in terms of Gaussian weak-lensing
observables. An inability to distinguish viable models that are based on
different physics potentially limits a deeper understanding of the fundamental
nature of cosmic acceleration. For a fixed redshift of sources, we demonstrate
that a machine learning network trained on simulated convergence maps can
discriminate between such models better than conventional higher-order
statistics. Results improve further when multiple source redshifts are
combined. To accelerate training, we implement a novel data compression
strategy that incorporates our prior knowledge of the morphology of typical
convergence map features. Our method fully distinguishes $\Lambda$CDM from its
most similar MG model on noise-free data, and it correctly identifies among the
MG models with at least 80% accuracy when using the full redshift information.
Adding noise lowers the correct classification rate of all models, but the
neural network still significantly outperforms the peak statistics used in a
previous analysis.
