  In astronomy, neural networks are often trained on simulated data with the
prospect of being applied to real observations. Unfortunately, simply training
a deep neural network on images from one domain does not guarantee satisfactory
performance on new images from a different domain. The ability to share
cross-domain knowledge is the main advantage of modern deep domain adaptation
techniques. Here we demonstrate the use of two techniques - Maximum Mean
Discrepancy (MMD) and adversarial training with Domain Adversarial Neural
Networks (DANN) - for the classification of distant galaxy mergers from the
Illustris-1 simulation, where the two domains presented differ only due to
inclusion of observational noise. We show how the addition of either MMD or
adversarial training greatly improves the performance of the classifier on the
target domain when compared to conventional machine learning algorithms,
thereby demonstrating great promise for their use in astronomy.
