# Strong Lens Discoveries in DESI Legacy Imaging Surveys DR10 with Two Deep Learning Architectures

**Authors:** Jose Carlos Inchausti, Christopher J. Storfer, Xiaosheng Huang, Yuan-Ming Hsu, Brandt Kaufmann, Chaitanya Pasupala, S. Banka, A. Dey, D. Lang, A. Meisner, J. Moustakas, A.D. Myers, E. F. Schlafly, D.J. Schlegel

arXiv: 2508.20087 · 2025-08-28

## TL;DR

This study employs deep learning models to identify strong gravitational lens candidates across nearly the entire southern extragalactic sky in DESI Legacy Imaging Surveys DR10, resulting in the discovery of 811 new candidates and a total of 3868 across multiple papers.

## Contribution

First to use ensemble deep learning models for comprehensive lens candidate search in DESI Legacy Surveys DR10 covering ~14,000 deg².

## Key findings

- Discovered 811 new strong lens candidates.
- Combined with previous papers, identified a total of 3868 candidates.
- Applied ensemble neural networks with visual inspection for candidate ranking.

## Abstract

We have conducted a search for strong gravitational lensing systems in the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 10 (DR10). This paper is the fourth in a series of searches (following Huang et al. 2020; Huang et al. 2021; Storfer et al. 2024, Paper I, II, & III respectively). This is the first catalog of lens candidates covering nearly the entirety of the extragalactic sky south of declination $\delta\approx +32$ deg, all of it observed by the DECam, covering $\sim$14,000 $deg^2$. We impose a $z$-band magnitude cut of < 20 in AB magnitude. We deploy a Residual Neural Network and EfficientNet as an ensemble trained on a compilation of known lensing systems and high-grade candidates as well as nonlenses in the same footprint. The predictions from these two base models are aggregated using a meta-learner. After applying our ensemble to the survey data, we exclude known candidates and systems, and use our own visual inspection portal to rank images in the top 0.01 percentile of all neural network recommendations. We have found 811 new lens candidates. These include 484 new candidates in the Legacy Surveys DR9 footprint, all parts of which have been searched for strong lenses at least once before, either by our group or others. Combining the discoveries from this work with those from Paper I (335), II (1210), and III (1512), we have discovered a total of 3868 new candidates in the DESI Legacy Surveys.

## Full text

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## Figures

15 figures with captions in the complete paper: https://tomesphere.com/paper/2508.20087/full.md

## References

104 references — full list in the complete paper: https://tomesphere.com/paper/2508.20087/full.md

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Source: https://tomesphere.com/paper/2508.20087