# An extended catalog of galaxy-galaxy strong gravitational lenses discovered in DES using convolutional neural networks

## TL;DR

This paper employs convolutional neural networks to identify galaxy-galaxy strong gravitational lenses in DES Year 3 data, significantly expanding the catalog of lens candidates through advanced machine learning techniques.

## Contribution

It introduces an improved neural network approach with new training sets, covering a broader redshift and color range, leading to a substantial increase in lens candidate identification.

## Key findings

- Identified 152 probable or definite lenses from high-scoring candidates.
- Discovered an additional 247 probable or definite candidates from lower-scoring images.
- Compiled a catalog of 511 galaxy-galaxy strong lens candidates.

## Abstract

We search Dark Energy Survey (DES) Year 3 imaging for galaxy-galaxy strong gravitational lenses using convolutional neural networks, extending previous work with new training sets and covering a wider range of redshifts and colors. We train two neural networks using images of simulated lenses, then use them to score postage stamp images of 7.9 million sources from the Dark Energy Survey chosen to have plausible lens colors based on simulations. We examine 1175 of the highest-scored candidates and identify 152 probable or definite lenses. Examining an additional 20,000 images with lower scores, we identify a further 247 probable or definite candidates. After including 86 candidates discovered in earlier searches using neural networks and 26 candidates discovered through visual inspection of blue-near-red objects in the DES catalog, we present a catalog of 511 lens candidates.