  We investigate and demonstrate the use of convolutional neural networks
(CNNs) for the task of distinguishing between merging and non-merging galaxies
in simulated images, and for the first time at high redshifts (i.e. $z=2$). We
extract images of merging and non-merging galaxies from the Illustris-1
cosmological simulation and apply observational and experimental noise that
mimics that from the Hubble Space Telescope; the data without noise form a
"pristine" data set and that with noise form a "noisy" data set. The test set
classification accuracy of the CNN is $79\%$ for pristine and $76\%$ for noisy.
The CNN outperforms a Random Forest classifier, which was shown to be superior
to conventional one- or two-dimensional statistical methods (Concentration,
Asymmetry, the Gini, $M_{20}$ statistics etc.), which are commonly used when
classifying merging galaxies. We also investigate the selection effects of the
classifier with respect to merger state and star formation rate, finding no
bias. Finally, we extract Grad-CAMs (Gradient-weighted Class Activation
Mapping) from the results to further assess and interrogate the fidelity of the
classification model.
