Josh Wilde

In preparation for future large surveys such as LSST and Euclid. These expect to find more than 10^5 gravitational lenses, I am interested in making sure the novel systems are discovered as these offer greater constraints on dark matter. I have been developing a CNN model to identify gravitational lenses from simulated Euclid images. This CNN model performs well with an F1 score of 0.98, but why? I have applied several approaches including deep dream, occlusion maps, and class generated images to understand the aspects of the image which influences the model’s classification. Currently I am creating images of compound lenses to understand how well my model performs on data of rare lens configurations.