HOLISMOKES XVI: Lens search in HSC-PDR3 with a neural network committee and post-processing for false-positive removal

Abstract

We have carried out a systematic search for galaxy-scale lenses exploiting multi-band imaging data from the third public data release of the Hyper Suprime-Cam (HSC) survey with the focus on false-positive removal, after applying deep learning classifiers to all 110 million sources with i-Kron radius above 0.8". To improve the performance, we tested the combination of multiple networks from our previous lens search projects and found the best performance by averaging the scores from five of our networks. Although this ensemble network leads already to a false-positive rate (FPR) of 0.01% at a true-positive rate (TPR) of 75% on known real lenses, we have elaborated techniques to further clean the network candidate list before visual inspection. In detail, we tested the rejection using SExtractor and the modeling network from HOLISMOKES IX, which resulted together in a candidate rejection of 29% without lowering the TPR. We carried out a comprehensive multi-stage visual inspection involving eight individuals and identified 95 grade A (average grade G >2.5) and 503 grade B (2.5 >G >1.5) lens candidates, including 92 discoveries reported for the first time. This inspection also incorporated a novel environmental characterization using histograms of photometric redshifts. We publicly release the average grades, mass model predictions, and environment characterization of all visually inspected candidates, while including references for previously discovered systems, which makes this catalog one of the largest compilation of known lenses. The results demonstrate that (1) the combination of multiple networks enhances the selection performance and (2) both automated masking tools as well as modeling networks, which can be easily applied to hundreds of thousands of network candidates, help reduce the number of false positives that is the main limitation in lens search to date.

arXiv comment10+9 pages, 7+7 figures, accepted by A&A

References in corpus (74)

• SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
• Array Programming with NumPy
• Astropy: A Community Python Package for Astronomy
• The NumPy array: a structure for efficient numerical computation
• Binary companions of evolved stars in APOGEE DR14: Search method and catalog of ~5,000 companions
• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• H0LiCOW XIII. A 2.4% measurement of $H_0$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes
• H0LiCOW V. New COSMOGRAIL time delays of HE0435-1223: $H_0$ to 3.8% precision from strong lensing in a flat $\Lambda$CDM model
• Photometric Redshifts for Hyper Suprime-Cam Subaru Strategic Program Data Release 1
• Cosmology from the Chinese Space Station Optical Survey (CSS-OS)
• The Sloan Lens ACS Survey. VII. Elliptical Galaxy Scaling Laws from Direct Observational Mass Measurements
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• The Halos of Satellite Galaxies: the Companion of the Massive Elliptical Lens SL2S J08544-0121
• The CFHTLS Strong Lensing Legacy Survey: I. Survey overview and T0002 release sample
• The MUSE Hubble Ultra Deep Field Survey: II. Spectroscopic redshifts and comparisons to color selections of high-redshift galaxies
• Disentangling Baryons and Dark Matter in the Spiral Gravitational Lens B1933+503
• Finding strong lenses in CFHTLS using convolutional neural networks
• Constraints on the Hubble constant from Supernova Refsdal's reappearance
• Dark matter halos of massive elliptical galaxies at $z\sim 0.2$ are well described by the Navarro-Frenk-White profile
• RingFinder: automated detection of galaxy-scale gravitational lenses in ground-based multi-filter imaging data
• Space Warps II. New Gravitational Lens Candidates from the CFHTLS Discovered through Citizen Science
• COSMOGRAIL XVI: Time delays for the quadruply imaged quasar DES J0408-5354 with high-cadence photometric monitoring
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• The Sloan Lens ACS Survey. XIII. Discovery of 40 New Galaxy-Scale Strong Lenses
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). I. Automatic search for galaxy-scale strong lenses
• Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey
• Sloan Digital Sky Survey Spectroscopic Lens Search. I. Discovery of Intermediate-Redshift Star-Forming Galaxies Behind Foreground Luminous Red Galaxies
• Measuring the value of the Hubble constant "à la Refsdal"
• Discovering New Strong Gravitational Lenses in the DESI Legacy Imaging Surveys
• COSMOGRAIL XIX: Time delays in 18 strongly lensed quasars from 15 years of optical monitoring
• Strong lensing models of eight CLASH clusters from extensive spectroscopy: accurate total mass reconstructions in the cores
• The MUSE Deep Lensed Field on the Hubble Frontier Field MACS~J0416
• 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
• The BOSS Emission-Line Lens Survey. III. : Strong Lensing of Ly$\alpha$ Emitters by Individual Galaxies
• 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). VI. Crowdsourced lens finding with Space Warps
• Exploring the physical properties of lensed star-forming clumps at $2\lesssim z\lesssim 6$
• Estimating Luminosities and Stellar Masses of Galaxies Photometrically without Determining Redshifts
• HOLISMOKES -- I. Highly Optimised Lensing Investigations of Supernovae, Microlensing Objects, and Kinematics of Ellipticals and Spirals
• Photometric Redshift Estimation with a Convolutional Neural Network: NetZ
• Strong lens systems search in the Dark Energy Survey using Convolutional Neural Networks
• 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
• Mining for Strong Gravitational Lenses with Self-supervised Learning
• Lensed Type Ia Supernova "Encore" at z=2: The First Instance of Two Multiply-Imaged Supernovae in the Same Host Galaxy
• HOLISMOKES. VIII. High-redshift, strong-lens search in the Hyper Suprime-Cam Subaru Strategic Program
• Cosmography with Supernova Refsdal through time-delay cluster lensing: independent measurements of the Hubble constant and geometry of the Universe
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). IV. Lensed quasar search in the HSC survey
• TDCOSMO. IX. Systematic comparison between lens modelling software programs: time delay prediction for WGD 2038-4008
• 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
• The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey: A Catalogue of Strong Galaxy-Galaxy Lens Candidates
• SDSS J0909$+$4449: A Large-separation Strongly Lensed Quasar at $z\sim 2.8$ with Three Images
• The PCA Lens-Finder: application to CFHTLS
• Survey of Gravitationally-lensed Objects in HSC Imaging (SuGOHI). VIII. New galaxy-scale lenses from the HSC SSP
• New Strong Gravitational Lenses from the DESI Legacy Imaging Surveys Data Release 9
• Galaxy And Mass Assembly (GAMA) Blended Spectra Catalog: Strong Galaxy-Galaxy Lens and Occulting Galaxy Pair Candidates
• New strong lensing modelling of SDSS J2222+2745 enhanced with VLT/MUSE spectroscopy
• Constraining the multi-scale dark-matter distribution in CASSOWARY 31 with strong gravitational lensing and stellar dynamics
• The impact of human expert visual inspection on the discovery of strong gravitational lenses
• Euclid: The Early Release Observations Lens Search Experiment
• Using deep Residual Networks to search for galaxy-Lyα emitter lens candidates based on spectroscopic-selection
• Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks
• LESSER: A Catalogue of Spectroscopically selected sample of Lyman-$\alpha$ Emitters Lensed By Galaxies
• HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
• HOLISMOKES -- X. Comparison between neural network and semi-automated traditional modeling of strong lenses
• Discovery of an unusually compact lensed Lyman Break Galaxy from the Hyper Suprime-Cam Survey
• HOLISMOKES -- XI. Evaluation of supervised neural networks for strong-lens searches in ground-based imaging surveys
• TEGLIE: Transformer encoders as strong gravitational lens finders in KiDS
• A Bayesian Approach to Strong Lens Finding in the Era of Wide-area Surveys
• HOLISMOKES XIII: Strong-lens candidates at all mass scales and their environments from the Hyper-Suprime Cam and deep learning
• Galaxy Spectra neural Networks (GaSNets). I. Searching for strong lens candidates in eBOSS spectra using Deep Learning
• Accelerating lensed quasar discovery and modeling with physics-informed variational autoencoders

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• The revolution in strong lensing discoveries from Euclid