Domain Adaptation in Application to Gravitational Lens Finding

Abstract The next decade is expected to see a tenfold increase in the number of strong gravitational lenses, driven by new wide-field imaging surveys. To discover these rare objects, efficient automated detection methods need to be developed. In this work, we assess the performance of three domain adaptation (DA) techniques—adversarial discriminative DA, Wasserstein distance guided representation learning (WDGRL), and supervised domain adaptation (SDA)—in enhancing lens-finding algorithms trained on simulated data when applied to observations from the Hyper Suprime-Cam Subaru Strategic Program. We find that WDGRL combined with an equivariant-neural-network-based encoder provides the best performance in an unsupervised setting and that SDA is able to enhance the model’s ability to distinguish between lenses and common similar-looking false positives, such as spiral galaxies, which is crucial for future lens surveys.