# Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks

## TL;DR

This paper demonstrates the use of convolutional neural networks to automatically identify strong gravitational lenses in the KiDS survey, achieving promising candidate selection and potential for large-scale lens discovery.

## Contribution

First application of CNN-based morphological classification for strong lens detection in KiDS, with optimized recognition of lenses with Einstein radii over 1.4 arcsec.

## Key findings

- CNN retrieved 761 lens candidates from 21789 galaxies
- Correctly identified 2 out of 3 known lenses in the sample
- Estimated potential to find up to 2400 lenses in KiDS survey

## Abstract

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyze sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in $255$ square degrees of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii $\gtrsim 1.4$ arcsec, about twice the $r$-band seeing in KiDS. In a sample of $21789$ colour-magnitude selected Luminous Red Galaxies (LRG), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find $\sim100$ massive LRG-galaxy lenses at $z\lsim 0.4$ in KiDS when completed. In the most optimistic scenario this number can grow considerably (to maximally $\sim$2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.