A Bayesian Approach to Strong Lens Finding in the Era of Wide-area Surveys

Abstract

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from $\mathcal{O}(10^3)$ to $\mathcal{O}(10^5)$. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong-lens experts to remove false positives which is only set to increase with these surveys. In this work we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam (HSC) survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false positive rate of $10^{-3}$ can be achieved with a completeness of $46\%$, compared to $34\%$ for the best individual classifier. Given the large number of galaxy-galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses.

14 pages, 9 figures. Accepted for publication in MNRAS

References in corpus (27)

• Wide-Field InfrarRed Survey Telescope-Astrophysics Focused Telescope Assets WFIRST-AFTA 2015 Report
• The Sloan Lens ACS Survey. V. The Full ACS Strong-Lens Sample
• Third Data Release of the Hyper Suprime-Cam Subaru Strategic Program
• The Wide Field Infrared Survey Telescope: 100 Hubbles for the 2020s
• First catalog of strong lens candidates in the COSMOS field
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Arcfinder: An algorithm for the automatic detection of gravitational arcs
• Gravitational lenses and lens candidates identified from the COSMOS field
• A lensed radio jet at milli-arcsecond resolution II: Constraints on fuzzy dark matter from an extended gravitational arc
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VI. Crowdsourced lens finding with Space Warps
• Unscrambling the lensed galaxies in JWST images behind SMACS0723
• High-quality strong lens candidates in the final Kilo Degree survey footprint
• 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
• HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
• Hierarchical Inference With Bayesian Neural Networks: An Application to Strong Gravitational Lensing
• A lensed radio jet at milli-arcsecond resolution I: Bayesian comparison of parametric lens models
• Deep Learning for Strong Lensing Search: Tests of the Convolutional Neural Networks and New Candidates from KiDS DR3
• Strong lensing in UNIONS: Toward a pipeline from discovery to modeling
• The velocity dispersion function of early-type galaxies and its redshift evolution: the newest results from lens redshift test
• Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant
• Hubble Asteroid Hunter: II. Identifying strong gravitational lenses in HST images with crowdsourcing
• The frequency of dust lanes in edge-on spiral galaxies identified by Galaxy Zoo in KiDS imaging of GAMA targets
• The impact of human expert visual inspection on the discovery of strong gravitational lenses
• Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks
• On the detectability of strong lensing in near-infrared surveys
• Survey of Gravitationally lensed Objects in HSC Imaging (SuGOHI) $-$ X. Strong Lens Finding in The HSC-SSP using Convolutional Neural Networks

Cited by in corpus (4)

• A model for galaxy-galaxy strong lensing statistics in surveys
• TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS
• HOLISMOKES XVI: Lens search in HSC-PDR3 with a neural network committee and post-processing for false-positive removal
• Galaxy-scale lens search in the PEARLS NEP TDF and CEERS JWST fields