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Strong gravitational lenses allow us to peer into the farthest reaches ofspace by bending the light from a background object around a massive object inthe foreground. Unfortunately, these lenses are extremely rare, and manuallyfinding them in astronomy surveys is difficult and time-consuming. We are thustasked with finding them in an automated fashion with few if any, known lensesto form positive samples. To assist us with training, we can simulate realisticlenses within our survey images to form positive samples. Naively training aResNet model with these simulated lenses results in a poor precision for thedesired high recall, because the simulations contain artifacts that are learnedby the model. In this work, we develop a lens detection method that combinessimulation, data augmentation, semi-supervised learning, and GANs to improvethis performance by an order of magnitude. We perform ablation studies andexamine how performance scales with the number of non-lenses and simulatedlenses. These findings allow researchers to go into a survey mostly ``blind"and still classify strong gravitational lenses with high precision and recall.

An Unsupervised Hunt for Gravitational Lenses