# Support Vector Machine classification of strong gravitational lenses

## TL;DR

This paper introduces an SVM-based method utilizing Gabor filters to efficiently identify strong gravitational lenses in large sky surveys, outperforming human examination in false positive rejection.

## Contribution

It presents a novel SVM approach with Gabor filters for lens detection, demonstrating high efficiency and effectiveness in large-scale simulated and real survey data.

## Key findings

- SVM with Gabor filters effectively rejects false positives.
- The method outperforms human examination in large datasets.
- Successful application to real survey images.

## Abstract

The imminent advent of very large-scale optical sky surveys, such as Euclid and 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 filterbank 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 100000 simulated lenses in a challenge dataset, and also apply the SVM method to survey images from the Kilo-Degree Survey.