A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method

Abstract

Automated arc detection methods are needed to scan the ongoing and next-generation wide-field imaging surveys, which are expected to contain thousands of strong lensing systems. Arc finders are also required for a quantitative comparison between predictions and observations of arc abundance. Several algorithms have been proposed to this end, but machine learning methods have remained as a relatively unexplored step in the arc finding process. In this work we introduce a new arc finder based on pattern recognition, which uses a set of morphological measurements derived from the Mediatrix Filamentation Method as entries to an Artificial Neural Network (ANN). We show a full example of the application of the arc finder, first training and validating the ANN on simulated arcs and then applying the code on four Hubble Space Telescope (HST) images of strong lensing systems. The simulated arcs use simple prescriptions for the lens and the source, while mimicking HST observational conditions. We also consider a sample of objects from HST images with no arcs in the training of the ANN classification. We use the training and validation process to determine a suitable set of ANN configurations, including the combination of inputs from the Mediatrix method, so as to maximize the completeness while keeping the false positives low. In the simulations the method was able to achieve a completeness of about 90% with respect to the arcs that are input to the ANN after a preselection. However, this completeness drops to $\sim$ 70% on the HST images. The false detections are of the order of 3% of the objects detected in these images. The combination of Mediatrix measurements with an ANN is a promising tool for the pattern recognition phase of arc finding. More realistic simulations and a larger set of real systems are needed for a better training and assessment of the efficiency of the method.

arXiv commentUpdated to match published version

References in corpus (75)

• A Universal Density Profile from Hierarchical Clustering
• The Structure of Cold Dark Matter Halos
• Detailed Decomposition of Galaxy Images. II. Beyond Axisymmetric Models
• The Dark Energy Survey: more than dark energy - an overview
• The Hubble Ultra Deep Field
• The statistics of LCDM Halo Concentrations
• The Sloan Lens ACS Survey. III - The Structure and Formation of Early-type Galaxies and their Evolution since z~1
• The Sloan Lens ACS Survey. I. A Large Spectroscopically Selected Sample of Massive Early-Type Lens Galaxies
• The redshift dependence of the structure of massive LCDM halos
• Galaxies in the Hubble Ultra Deep Field: I. Detection, Multiband Photometry, Photometric Redshifts, and Morphology
• The population of galaxy-galaxy strong lenses in forthcoming optical imaging surveys
• The first and second data releases of the Kilo-Degree Survey
• The internal structure and formation of early-type galaxies: the gravitational--lens system MG2016+112 at z=1.004
• The Blanco Cosmology Survey: Data Acquisition, Processing, Calibration, Quality Diagnostics and Data Release
• Cosmological Constraints from Strong Gravitational Lensing in Galaxy Clusters
• The BOSS Emission-Line Lens Survey (BELLS). I. A large spectroscopically selected sample of Lens Galaxies at redshift ~ 0.5
• The CFHTLS Strong Lensing Legacy Survey: I. Survey overview and T0002 release sample
• The internal structure of the lens PG1115+080: breaking degeneracies in the value of the Hubble Constant
• The formation and assembly of a typical star-forming galaxy at z~3
• GALAPAGOS: From Pixels to Parameters
• The CFHTLS-Strong Lensing Legacy Survey (SL2S): Investigating the group-scale lenses with the SARCS sample
• Cosmology with Strong Lensing Systems
• First catalog of strong lens candidates in the COSMOS field
• The Spatial Structure of An Accretion Disk
• CLASH-VLT: A Highly Precise Strong Lensing Model of the Galaxy Cluster RXC J2248.7-4431 (Abell S1063) and Prospects for Cosmography
• The Incidence of Strong-Lensing Clusters in the Red-Sequence Cluster Survey
• Search for the neutral Higgs bosons of the Minimal Supersymmetric Standard Model in pp collisions at sqrt(s)=7 TeV with the ATLAS detector
• 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
• A New Survey for Giant Arcs
• A Systematic Search for Gravitationally-Lensed Arcs in the Hubble Space Telescope WFPC2 Archive
• Galaxy-Scale Strong Lensing Tests of Gravity and Geometric Cosmology: Constraints and Systematic Limitations
• The Highest Resolution Mass Map of Galaxy Cluster Substructure To Date Without Assuming Light Traces Mass: LensPerfect Analysis of Abell 1689
• A search for gravitational lensing in 38 X-ray selected clusters of galaxies
• Automated detection of galaxy-scale gravitational lenses in high resolution imaging data
• Super-resolving distant galaxies with gravitational telescopes: Keck-LGSAO and Hubble imaging of the lens system SDSSJ0737+3216
• Two New Large Separation Gravitational Lenses from SDSS
• Arcfinder: An algorithm for the automatic detection of gravitational arcs
• Substructure in lensing clusters and simulations
• On the Incidence of Strong Gravitational Lensing by Clusters in the Las Campanas Distant Cluster Survey
• Gravitational lenses and lens candidates identified from the COSMOS field
• Constraining the Cosmological Parameters using Strong Lensing
• Arc Statistics
• PyMorph: Automated Galaxy Structural Parameter Estimation using Python
• The Top Ten List of Gravitational Lens Candidates from the HST Medium Deep Survey
• 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
• Automatic detection of arcs and arclets formed by gravitational lensing
• A PCA-based automated finder for galaxy-scale strong lenses
• Non-parametric Reconstruction of Cluster Mass Distribution from Strong Lensing: Modelling Abell 370
• Broadband Photometry of 105 Giant Arcs: Redshift Constraints and Implications for Giant Arc Statistics
• Gravitational lensing as a probe of cold dark matter subhalos
• Discovery of a faint, star-forming, multiply lensed, Lyman-alpha blob
• Strong Gravitational Lens Candidates in the GOODS ACS Fields
• The Sloan Bright Arcs Survey : Discovery of Seven New Strongly Lensed Galaxies from z=0.66-2.94
• The OLS-lens survey: The discovery of five new galaxy-galaxy strong lenses from the SDSS
• CLASH-VLT: Testing the Nature of Gravity with Galaxy Cluster Mass Profiles
• Strong Gravitational Lensing by the Super-massive cD Galaxy in Abell 3827
• A candidate gravitational lens in the Hubble Deep Field
• A new tool for image analysis based on Chebyshev rational functions: CHEF functions
• Strong lensing constraints on bimetric massive gravity
• Observation and Confirmation of Six Strong Lensing Systems in The Dark Energy Survey Science Verification Data
• New insight on galaxy structure from GALPHAT I. Motivation, methodology, and benchmarks for Sersic models
• The PCA Lens-Finder: application to CFHTLS
• Constraints on Dark Energy Models from Galaxy Clusters with Multiple Arcs
• A magnified glance into the dark sector: probing cosmological models with strong lensing in A1689
• The Detection and Statistics of Giant Arcs Behind CLASH Clusters
• Lensed arc statistics: comparison of Millennium-simulation galaxy clusters to Hubble Space Telescope observations of an X-ray selected sample
• Multi-color detection of gravitational arcs
• A method to search for strong galaxy-galaxy lenses in optical imaging surveys
• Feasibility of Probing Dark Energy with Strong Gravitational Lensing Systems -- Fisher-Matrix Approach --
• Extensive light profile fitting of galaxy-scale strong lenses
• The SOAR Gravitational Arc Survey - I: Survey overview and photometric catalogs
• Galaxy clusters in the SDSS Stripe 82 based on galaxy photometric redshifts
• ARCRAIDER II: Arc search in a sample of non-Abell clusters

Cited by in corpus (26)

• 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
• The Strong Gravitational Lens Finding Challenge
• Finding strong lenses in CFHTLS using convolutional neural networks
• Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
• The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples
• HOLISMOKES -- IV. Efficient mass modeling of strong lenses through deep learning
• Hubble Asteroid Hunter: II. Identifying strong gravitational lenses in HST images with crowdsourcing
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• VICS82: the VISTA-CFHT Stripe 82 near-infrared survey
• Automated Lensing Learner: Automated Strong Lensing Identification with a Computer Vision Technique
• ROGER: Reconstructing Orbits of Galaxies in Extreme Regions using machine learning techniques
• EasyCritics I: Efficient detection of strongly-lensing galaxy groups and clusters in wide-field surveys
• Identification of Galaxy-Galaxy Strong Lens Candidates in the DECam Local Volume Exploration Survey Using Machine Learning
• Auto-identification of unphysical source reconstructions in strong gravitational lens modelling
• HOLISMOKES -- IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images
• Deep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1
• The DES Bright Arcs Survey: Candidate Strongly Lensed Galaxy Systems from the Dark Energy Survey 5,000 Sq. Deg. Footprint
• Detection of Strongly Lensed Arcs in Galaxy Clusters with Transformers
• Deep Learning in Wide-field Surveys: Fast Analysis of Strong Lenses in Ground-based Cosmic Experiments
• Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge
• Deep Learning Blazar Classification based on Multi-frequency Spectral Energy Distribution Data
• A Modular Deep Learning Pipeline for Galaxy-Scale Strong Gravitational Lens Detection and Modeling
• Strong lensing cross sections for isothermal models - I. Finite source effects in the circular case
• Non-Sequential Neural Network for Simultaneous, Consistent Classification and Photometric Redshifts of OTELO Galaxies
• Search for strong galaxy-galaxy lensing in SDSS-III BOSS