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In astronomy, neural networks are often trained on simulated data with theprospect of being applied to real observations. Unfortunately, simply traininga deep neural network on images from one domain does not guarantee satisfactoryperformance on new images from a different domain. The ability to sharecross-domain knowledge is the main advantage of modern deep domain adaptationtechniques. Here we demonstrate the use of two techniques - Maximum MeanDiscrepancy (MMD) and adversarial training with Domain Adversarial NeuralNetworks (DANN) - for the classification of distant galaxy mergers from theIllustris-1 simulation, where the two domains presented differ only due toinclusion of observational noise. We show how the addition of either MMD oradversarial training greatly improves the performance of the classifier on thetarget domain when compared to conventional machine learning algorithms,thereby demonstrating great promise for their use in astronomy.

Domain adaptation techniques for improved cross-domain study of galaxy mergers