# LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks

## TL;DR

This paper introduces LinKS, a new galaxy-scale strong lens candidate catalog from the KiDS survey, using convolutional neural networks to efficiently identify potential lenses with minimal human oversight.

## Contribution

The study develops and applies ConvNets to large survey data, producing a high-quality lens candidate list with a scalable method for future large-scale surveys like Euclid and LSST.

## Key findings

- Identified 1983 potential lens candidates from KiDS data.
- Discovered 89 high-confidence strong lens candidates through human inspection.
- Projected to find ~3000 lens candidates in future surveys with low false positives.

## Abstract

We present a new sample of galaxy-scale strong gravitational-lens candidates, selected from 904 square degrees of Data Release 4 of the Kilo-Degree Survey (KiDS), i.e., the "Lenses in the Kilo-Degree Survey" (LinKS) sample. We apply two Convolutional Neural Networks (ConvNets) to $\sim88\,000$ colour-magnitude selected luminous red galaxies yielding a list of 3500 strong-lens candidates. This list is further down-selected via human inspection. The resulting LinKS sample is composed of 1983 rank-ordered targets classified as "potential lens candidates" by at least one inspector. Of these, a high-grade subsample of 89 targets is identified with potential strong lenses by all inspectors. Additionally, we present a collection of another 200 strong lens candidates discovered serendipitously from various previous ConvNet runs. A straightforward application of our procedure to future Euclid or LSST data can select a sample of $\sim3000$ lens candidates with less than 10 per cent expected false positives and requiring minimal human intervention.