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

Understand

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.