2017MNRAS.471.3378H

The imminent advent of very large-scale optical sky surveys, such as Euclid and the Large Synoptic Survey Telescope (LSST), makes it important to find efficient ways of discovering rare objects such as strong gravitational lens systems, where a background object is multiply gravitationally imaged by a foreground mass. As well as finding the lens systems, it is important to reject false positives due to intrinsic structure in galaxies, and much work is in progress with machine learning algorithms such as neural networks in order to achieve both these aims. We present and discuss a support vector machine (SVM) algorithm which makes use of a Gabor filter bank in order to provide learning criteria for separation of lenses and non-lenses, and demonstrate using blind challenges that under certain circumstances, it is a particularly efficient algorithm for rejecting false positives. We compare the SVM engine with a large-scale human examination of 100 000 simulated lenses in a challenge data set, and also apply the SVM method to survey images from the Kilo Degree Survey.