Convolutional neural network identification of galaxy post-mergers in
  UNIONS using IllustrisTNG
  The Canada-France Imaging Survey (CFIS) will consist of deep, high-resolution
r-band imaging over ~5000 square degrees of the sky, representing a first-rate
opportunity to identify recently-merged galaxies. Due to the large number of
galaxies in CFIS, we investigate the use of a convolutional neural network
(CNN) for automated merger classification. Training samples of post-merger and
isolated galaxy images are generated from the IllustrisTNG simulation processed
with the observational realism code RealSim. The CNN's overall classification
accuracy is 88 percent, remaining stable over a wide range of intrinsic and
environmental parameters. We generate a mock galaxy survey from IllustrisTNG in
order to explore the expected purity of post-merger samples identified by the
CNN. Despite the CNN's good performance in training, the intrinsic rarity of
post-mergers leads to a sample that is only ~6 percent pure when the default
decision threshold is used. We investigate trade-offs in purity and
completeness with a variable decision threshold and find that we recover the
statistical distribution of merger-induced star formation rate enhancements.
Finally, the performance of the CNN is compared with both traditional automated
methods and human classifiers. The CNN is shown to outperform Gini-M20 and
asymmetry methods by an order of magnitude in post-merger sample purity on the
mock survey data. Although the CNN outperforms the human classifiers on sample
completeness, the purity of the post-merger sample identified by humans is
frequently higher, indicating that a hybrid approach to classifications may be
an effective solution to merger classifications in large surveys.
