  Understanding morphological types of galaxies is a key parameter for studying
their formation and evolution. Neural networks that have been used previously
for galaxy morphology classification have some disadvantages, such as not being
invariant under rotation. In this work, we studied the performance of Capsule
Network, a recently introduced neural network architecture that is rotationally
invariant and spatially aware, on the task of galaxy morphology classification.
We designed two evaluation scenarios based on the answers from the question
tree in the Galaxy Zoo project. In the first scenario, we used Capsule Network
for regression and predicted probabilities for all of the questions. In the
second scenario, we chose the answer to the first morphology question that had
the highest user agreement as the class of the object and trained a Capsule
Network classifier, where we also reconstructed galaxy images. We achieved
promising results in both of these scenarios. Automated approaches such as the
one introduced here will greatly decrease the workload of astronomers and will
play a critical role in the upcoming large sky surveys.
