Automatic detection of arcs and arclets formed by gravitational lensing

Abstract

We present an algorithm developed particularly to detect gravitationally lensed arcs in clusters of galaxies. This algorithm is suited for automated surveys as well as individual arc detections. New methods are used for image smoothing and source detection. The smoothing is performed by so-called anisotropic diffusion, which maintains the shape of the arcs and does not disperse them. The algorithm is much more efficient in detecting arcs than other source finding algorithms and the detection by eye.

A&A in press, 12 pages, 16 figures

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Cited by in corpus (36)

• Gravitational Lensing
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• The CFHTLS Strong Lensing Legacy Survey: I. Survey overview and T0002 release sample
• The CFHTLS-Strong Lensing Legacy Survey (SL2S): Investigating the group-scale lenses with the SARCS sample
• Finding strong lenses in CFHTLS using convolutional neural networks
• Space Warps II. New Gravitational Lens Candidates from the CFHTLS Discovered through Citizen Science
• LinKS: Discovering galaxy-scale strong lenses in the Kilo-Degree Survey using Convolutional Neural Networks
• Finding high-redshift strong lenses in DES using convolutional neural networks
• Deep Convolutional Neural Networks as strong gravitational lens detectors
• Arcfinder: An algorithm for the automatic detection of gravitational arcs
• LensExtractor: A Convolutional Neural Network in Search of Strong Gravitational Lenses
• Arc Statistics
• Using Convolutional Neural Networks to identify Gravitational Lenses in Astronomical images
• The Lensed Arc Production Efficiency of Galaxy Clusters: A Comparison of Matched Observed and Simulated Samples
• A Systematic Search for High Surface Brightness Giant Arcs in a Sloan Digital Sky Survey Cluster Sample
• Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
• On building a cluster watch-list for identifying strongly lensed supernovae, gravitational waves and kilonovae
• Strong lensing statistics and the power spectrum normalisation
• A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method
• The SNAP Strong Lens Survey
• Observation and Confirmation of Six Strong Lensing Systems in The Dark Energy Survey Science Verification Data
• The Detection and Statistics of Giant Arcs Behind CLASH Clusters
• A method to search for strong galaxy-galaxy lenses in optical imaging surveys
• Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
• EasyCritics I: Efficient detection of strongly-lensing galaxy groups and clusters in wide-field surveys
• Using deep Residual Networks to search for galaxy-Lyα emitter lens candidates based on spectroscopic-selection
• Finding Strong Gravitational Lenses Through Self-Attention
• Strong lensing in the MareNostrum Universe II: scaling relations and optical depths
• The SDSS Discovery of a Strongly Lensed Post-Starburst Galaxy at z=0.766
• Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
• The lensing efficiencies of MACS X-ray selected versus RCS optically selected galaxy clusters
• ARCRAIDER II: Arc search in a sample of non-Abell clusters
• Selection effects on X-ray and strong-lensing clusters in various cosmologies
• ARCRAIDER I: Detailed optical and X-ray analysis of the cooling flow cluster Z3146
• Machine learning and Kolmogorov analysis to reveal gravitational lenses
• Keys to Cosmology - Clusters of Galaxies