Mining for Strong Gravitational Lenses with Self-supervised Learning

Abstract

We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Release 9. Targeting the identification of new strong gravitational lens candidates, we first create a rapid similarity search tool to discover new strong lenses given only a single labelled example. We then show how training a simple linear classifier on the self-supervised representations, requiring only a few minutes on a CPU, can automatically classify strong lenses with great efficiency. We present 1192 new strong lens candidates that we identified through a brief visual identification campaign, and release an interactive web-based similarity search tool and the top network predictions to facilitate crowd-sourcing rapid discovery of additional strong gravitational lenses and other rare objects: https://github.com/georgestein/ssl-legacysurvey.

arXiv comment24 Pages, 15 figures, published in ApJ, data at github.com/georgestein/ssl-legacysurvey

References in corpus (35)

• Maps of Dust IR Emission for Use in Estimation of Reddening and CMBR Foregrounds
• Overview of the DESI Legacy Imaging Surveys
• Galaxy Zoo : Morphologies derived from visual inspection of galaxies from the Sloan Digital Sky Survey
• H0LiCOW XIII. A 2.4% measurement of $H_0$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes
• Galaxy Zoo 2: detailed morphological classifications for 304,122 galaxies from the Sloan Digital Sky Survey
• Strong Lensing by Galaxies
• The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
• The Clustering of DESI-like Luminous Red Galaxies Using Photometric Redshifts
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
• Galaxy Zoo: Probabilistic Morphology through Bayesian CNNs and Active Learning
• The Strong Gravitational Lens Finding Challenge
• Finding strong lenses in CFHTLS using convolutional neural networks
• Probing dark matter structure down to $10^7$ solar masses: flux ratio statistics in gravitational lenses with line of sight halos
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). I. Automatic search for galaxy-scale strong lenses
• Finding high-redshift strong lenses in DES using convolutional neural networks
• Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey
• Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys
• Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
• Self-Supervised Representation Learning for Astronomical Images
• 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
• LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). II. Environments and Line-of-Sight Structure of Strong Gravitational Lens Galaxies to z ~ 0.8
• Survey of gravitationally-lensed objects in HSC imaging (SuGOHI). III. Statistical strong lensing constraints on the stellar IMF of CMASS galaxies
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VI. Crowdsourced lens finding with Space Warps
• High-quality strong lens candidates in the final Kilo Degree survey footprint
• HOLISMOKES. VI. New galaxy-scale strong lens candidates from the HSC-SSP imaging survey
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). V. Group-to-cluster scale lens search from the HSC-SSP Survey
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). IV. Lensed quasar search in the HSC survey
• Pushing automated morphological classifications to their limits with the Dark Energy Survey
• Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
• ALMA Lensing Cluster Survey: an ALMA galaxy signposting a MUSE galaxy group at z=4.3 behind 'El Gordo'
• VLT/Magellan spectroscopy of 29 strong lensing selected galaxy clusters

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• Towards an astronomical foundation model for stars with a Transformer-based model
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• Euclid: The Early Release Observations Lens Search Experiment
• Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks
• Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
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• A model for galaxy-galaxy strong lensing statistics in surveys
• TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS
• ULISSE: A Tool for One-shot Sky Exploration and its Application to Active Galactic Nuclei Detection
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• HOLISMOKES XVI: Lens search in HSC-PDR3 with a neural network committee and post-processing for false-positive removal
• Detection of Unresolved Strongly Lensed Supernovae with 7-Dimensional Telescope
• Optical+NIR analysis of a Newly Confirmed Einstein ring at z$\sim$1 from the Kilo-Degree Survey: Dark matter fraction, total and dark matter density slope and IMF
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• Radio Galaxy Zoo: Morphological classification by Fanaroff-Riley designation using self-supervised pre-training
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