Explaining deep learning of galaxy morphology with saliency mapping
  We successfully demonstrate the use of explainable artificial intelligence
(XAI) techniques on astronomical datasets in the context of measuring galactic
bar lengths. The method consists of training convolutional neural networks on
human classified data from Galaxy Zoo in order to predict general galaxy
morphologies, and then using SmoothGrad (a saliency mapping technique) to
extract the bar for measurement by a bespoke algorithm. We contrast this to
another method of using a convolutional neural network to directly predict
galaxy bar lengths. These methods achieved correlation coefficients of 0.76 and
0.59, and root mean squared errors of 1.69 and 2.10 respective to human
measurements. We conclude that XAI methods outperform conventional deep
learning in this case, which could be reasonably explained by the larger
datasets available when training the models. We suggest that our XAI method can
be used to extract other galactic features (such as the bulge-to-disk ratio)
without needing to collect new datasets or train new models. We also suggest
that these techniques can be used to refine deep learning models as well as
identify and eliminate bias within training datasets.
