Gravitational Lenses in UNIONS and Euclid (GLUE) I: A Search for Strong Gravitational Lenses in UNIONS with Subaru, CFHT, and Pan-STARRS Data

TL;DR

This paper introduces a deep learning pipeline for discovering strong gravitational lenses in the UNIONS survey, resulting in over 1300 lens candidates, some with spectroscopic confirmation, advancing ground-based lens detection methods.

Contribution

The study develops a ResNet-based neural network trained on real lenses for multi-band ground-based survey data, achieving large-scale lens candidate identification in UNIONS.

Findings

Discovered 1346 new strong lens candidates.

Confirmed seven high-redshift lensed sources spectroscopically.

Identified 283 candidates with SDSS redshifts and 297 with DESI redshifts.

Abstract

We present the results of our pipeline for discovering strong gravitational lenses in the ongoing Ultraviolet Near-Infrared Optical Northern Survey (UNIONS). We successfully train the deep residual neural network (ResNet) based on CMU-Deeplens architecture, which is designed to detect strong lenses in ground-based imaging surveys. We train on images of real strong lenses and deploy on a sample of 8 million galaxies in areas with full coverage in the g, r, and i filters, the first multi-band search for strong gravitational lenses in UNIONS. Following human inspection and grading, we report the discovery of a total of 1346 new strong lens candidates of which 146 are grade A, 199 grade B, and 1001 grade C. Of these candidates, 283 have lens-galaxy spectroscopic redshifts from the Sloan Digital Sky Survey (SDSS) and an additional 297 from the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1). We find 15 of these systems display evidence of both lens and source galaxy redshifts in spectral superposition. We additionally report the spectroscopic confirmation of seven lensed sources in highquality systems, all with z > 2.1, using the Keck Near-Infrared Echelle Spectrograph (NIRES) and Gemini Near-Infrared Spectrograph (GNIRS).