# Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey

## TL;DR

This paper presents a semi-automated method using deep residual neural networks to discover new strong gravitational lensing systems in the extensive DECaLS survey data, resulting in 335 new candidates.

## Contribution

It introduces a neural network-based approach tailored for identifying strong lenses in large photometric surveys, applied to the DECaLS data, and reports new lens candidates.

## Key findings

- Identified 335 new strong lensing candidates.
- Successfully applied deep learning to large survey data.
- Demonstrated effectiveness of neural networks in lens detection.

## Abstract

We perform a semi-automated search for strong gravitational lensing systems in the 9,000 deg$^2$ Dark Energy Camera Legacy Survey (DECaLS), part of the DESI Legacy Imaging Surveys (Dey et al.). The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture (He et al.) developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training set that consists of known lensing systems in the Legacy Surveys and DES as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals (Lang et al.) in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.