Searching for strong lensing by late-type galaxies in UNIONS

Abstract

Recent wide-field galaxy surveys have led to an explosion in the number of galaxy-scale strong gravitational lens candidates. However, the vast majority of them feature massive luminous red galaxies as the main deflectors, with late-type galaxies being vastly under-represented. This work presents a dedicated search for lensing by edge-on late-type galaxies in the Ultraviolet Near Infrared Optical Northern Survey (UNIONS). The search covers $3600$ deg${}^2$ of $r$-band observations taken from the Canada-France-Hawaii Telescope. We considered all sources with magnitudes in the range $17<r<20.5$, without any colour pre-selection, yielding a parent sample of seven million sources. We characterised our parent sample via the visual inspection of $120000$ sources selected at random. From it, we estimate, with a 68% confidence interval, that 1 in every $30000$ sources is an edge-on lens candidate, with at least eight high-quality candidates in the parent sample. Our search relied on a convolutional neural network (CNN) to select a reduced sample of candidates, which we followed with a visual inspection to curate the final sample. The CNN was trained from scratch using simulated $r$-band observations of edge-on lenses, and real observations of non-lenses. We found 61 good edge-on lens candidates using the CNN. Moreover, combining the CNN candidates with those found serendipitously and those identified while characterising the parent sample, we discovered 4 grade A, 20 grade B, and 58 grade C edge-on lens candidates, effectively doubling the known sample of these systems. We also discovered 16 grade A, 16 grade B, and 18 grade C lens candidates of other types. Finally, based on the characterisation of the parent sample, we estimate that our search found around 60% of the bright grade A and B edge-on lens candidates within the parent sample.

arXiv commentReplacement after peer review. 25 pages, 11 figures. Accepted for publication in A&A

References in corpus (69)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• The Structure of Cold Dark Matter Halos
• The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package
• Overview of the DESI Legacy Imaging Surveys
• Sloan Digital Sky Survey IV: Mapping the Milky Way, Nearby Galaxies, and the Distant Universe
• H0LiCOW XIII. A 2.4% measurement of $H_0$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes
• Dark matter halo concentrations in the Wilkinson Microwave Anisotropy Probe year 5 cosmology
• The Pan-STARRS1 Database and Data Products
• The Sloan Lens ACS Survey. X. Stellar, Dynamical, and Total Mass Correlations of Massive Early-type Galaxies
• Euclid. I. Overview of the Euclid mission
• The Sloan Lens ACS Survey. I. A Large Spectroscopically Selected Sample of Massive Early-Type Lens Galaxies
• TDCOSMO IV: Hierarchical time-delay cosmography -- joint inference of the Hubble constant and galaxy density profiles
• The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
• Lenstronomy: multi-purpose gravitational lens modelling software package
• Detection of a Dark Substructure through Gravitational Imaging
• Cosmology from gravitational lens time delays and Planck data
• MegaPipe: the MegaCam image stacking pipeline at the Canadian Astronomical Data Centre
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• lenstronomy II: A gravitational lensing software ecosystem
• Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
• Disentangling Baryons and Dark Matter in the Spiral Gravitational Lens B1933+503
• The Strong Gravitational Lens Finding Challenge
• Constraints on the Hubble constant from Supernova Refsdal's reappearance
• Is every strong lens model unhappy in its own way? Uniform modelling of a sample of 13 quadruply+ imaged quasars
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• The bulge-halo conspiracy in massive elliptical galaxies: implications for the stellar initial mass function and halo response to baryonic processes
• Finding high-redshift strong lenses in DES using convolutional neural networks
• TDCOSMO. XII. Improved Hubble constant measurement from lensing time delays using spatially resolved stellar kinematics of the lens galaxy
• The Canada-France Imaging Survey: First results from the u-band component
• Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey
• The SWELLS survey. IV. Precision measurements of the stellar and dark matter distributions in a spiral lens galaxy
• Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys
• The SWELLS survey. II. Breaking the disk-halo degeneracy in the spiral galaxy gravitational lens SDSS J2141-0001
• The SWELLS survey - V. A Salpeter stellar initial mass function in the bulges of massive spiral galaxies
• JWST UNCOVER: Discovery of $z>9$ Galaxy Candidates Behind the Lensing Cluster Abell 2744
• The SWELLS Survey. I. A large spectroscopically selected sample of edge-on late-type lens galaxies
• HOLISMOKES -- II. Identifying galaxy-scale strong gravitational lenses in Pan-STARRS using convolutional neural networks
• New high-quality strong lens candidates with deep learning in the Kilo Degree Survey
• Gravitationally lensed quasars in Gaia -- IV. 150 new lenses, quasar pairs, and projected quasars
• Survey of gravitationally-lensed objects in HSC imaging (SuGOHI). III. Statistical strong lensing constraints on the stellar IMF of CMASS galaxies
• High-quality strong lens candidates in the final Kilo Degree survey footprint
• The SWELLS survey. III. Disfavouring "heavy" initial mass functions for spiral lens galaxies
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• HOLISMOKES. VI. New galaxy-scale strong lens candidates from the HSC-SSP imaging survey
• Mining for Strong Gravitational Lenses with Self-supervised Learning
• HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
• Automated galaxy-galaxy strong lens modelling: no lens left behind
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). IV. Lensed quasar search in the HSC survey
• Constraining the low-mass end of the Initial Mass Function with Gravitational Lensing
• Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
• The inner dark matter distribution of the Cosmic Horseshoe (J1148+1930) with gravitational lensing and dynamics
• Strong lensing in UNIONS: Toward a pipeline from discovery to modeling
• Abell 1201: Detection of an Ultramassive Black Hole in a Strong Gravitational Lens
• The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: A Catalogue of Strong Galaxy-Galaxy Lens Candidates
• ShapePipe: a new shape measurement pipeline and weak-lensing application to UNIONS/CFIS data
• A search for edge-on galaxy lenses in the CFHT Legacy Survey
• New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
• The SWELLS Survey. VI. hierarchical inference of the initial mass functions of bulges and discs
• The impact of human expert visual inspection on the discovery of strong gravitational lenses
• Euclid: The Early Release Observations Lens Search Experiment
• Two-dimensional kinematics of SLACS lenses - IV. The complete VLT-VIMOS data set
• Discovering strongly lensed quasar candidates with catalogue-based methods from DESI Legacy Surveys
• The DES Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5,000 Sq. Deg. Footprint
• Project Dinos II: Redshift evolution of dark and luminous matter density profiles in strong-lensing elliptical galaxies across 0.1 < z < 0.9
• HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
• A model for galaxy-galaxy strong lensing statistics in surveys
• Euclid Quick Data Release (Q1). The Strong Lensing Discovery Engine C: Finding lenses with machine learning
• TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS
• HOLISMOKES XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning