Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses
This work addresses false positives in gravitational lens detection for astronomers, but it is incremental as it shows no major improvement over existing methods.
The study compared binary and multi-class classification models for identifying strong gravitational lenses in Hubble Space Telescope images, finding no significant benefit to using multi-class models over binary ones, with results including potential new lens candidates.
Typically, binary classification lens-finding schemes are used to discriminate between lens candidates and non-lenses. However, these models often suffer from substantial false-positive classifications. Such false positives frequently occur due to images containing objects such as crowded sources, galaxies with arms, and also images with a central source and smaller surrounding sources. Therefore, a model might confuse the stated circumstances with an Einstein ring. It has been proposed that by allowing such commonly misclassified image types to constitute their own classes, machine learning models will more easily be able to learn the difference between images that contain real lenses, and images that contain lens imposters. Using Hubble Space Telescope (HST) images, in the F814W filter, we compare the usage of binary and multi-class classification models applied to the lens finding task. From our findings, we conclude there is not a significant benefit to using the multi-class model over a binary model. We will also present the results of a simple lens search using a multi-class machine learning model, and potential new lens candidates.