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