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Typically, binary classification lens-finding schemes are used todiscriminate between lens candidates and non-lenses. However, these modelsoften suffer from substantial false-positive classifications. Such falsepositives frequently occur due to images containing objects such as crowdedsources, galaxies with arms, and also images with a central source and smallersurrounding sources. Therefore, a model might confuse the stated circumstanceswith an Einstein ring. It has been proposed that by allowing such commonlymisclassified image types to constitute their own classes, machine learningmodels will more easily be able to learn the difference between images thatcontain real lenses, and images that contain lens imposters. Using Hubble SpaceTelescope (HST) images, in the F814W filter, we compare the usage of binary andmulti-class classification models applied to the lens finding task. From ourfindings, we conclude there is not a significant benefit to using themulti-class model over a binary model. We will also present the results of asimple lens search using a multi-class machine learning model, and potentialnew lens candidates.

Comparison of Multi-Class and Binary Classification Machine Learning Models in Identifying Strong Gravitational Lenses