DeepMerge II: Building Robust Deep Learning Algorithms for Merging Galaxy Identification Across Domains

Understand

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations.

• Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset.
• Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary.
• Here we employ domain adaptation techniques$-$ Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)$-$ and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies.