Developing a Victorious Strategy to the Second Strong Gravitational Lensing Data Challenge

Abstract

Strong Lensing is a powerful probe of the matter distribution in galaxies and clusters and a relevant tool for cosmography. Analyses of strong gravitational lenses with Deep Learning have become a popular approach due to these astronomical objects' rarity and image complexity. Next-generation surveys will provide more opportunities to derive science from these objects and an increasing data volume to be analyzed. However, finding strong lenses is challenging, as their number densities are orders of magnitude below those of galaxies. Therefore, specific Strong Lensing search algorithms are required to discover the highest number of systems possible with high purity and low false alarm rate. The need for better algorithms has prompted the development of an open community data science competition named Strong Gravitational Lensing Challenge (SGLC). This work presents the Deep Learning strategies and methodology used to design the highest-scoring algorithm in the II SGLC. We discuss the approach used for this dataset, the choice for a suitable architecture, particularly the use of a network with two branches to work with images in different resolutions, and its optimization. We also discuss the detectability limit, the lessons learned, and prospects for defining a tailor-made architecture in a survey in contrast to a general one. Finally, we release the models and discuss the best choice to easily adapt the model to a dataset representing a survey with a different instrument. This work helps to take a step towards efficient, adaptable and accurate analyses of strong lenses with deep learning frameworks.

arXiv comment14 pages, 12 figures

References in corpus (58)

• From dwarf spheroidals to cDs: Simulating the galaxy population in a LCDM cosmology
• H0LiCOW XIII. A 2.4% measurement of $H_0$ from lensed quasars: $5.3\sigma$ tension between early and late-Universe probes
• The Sloan Lens ACS Survey. III - The Structure and Formation of Early-type Galaxies and their Evolution since z~1
• Dissecting the Gravitational Lens B1608+656. II. Precision Measurements of the Hubble Constant, Spatial Curvature, and the Dark Energy Equation of State
• Gravitational detection of a low-mass dark satellite at cosmological distance
• Weighing simulated galaxy clusters using lensing and X-ray
• Fast Automated Analysis of Strong Gravitational Lenses with Convolutional Neural Networks
• The internal structure and formation of early-type galaxies: the gravitational--lens system MG2016+112 at z=1.004
• Galaxy Zoo DECaLS: Detailed Visual Morphology Measurements from Volunteers and Deep Learning for 314,000 Galaxies
• CMU DeepLens: Deep Learning For Automatic Image-based Galaxy-Galaxy Strong Lens Finding
• Cosmological Constraints from Strong Gravitational Lensing in Galaxy Clusters
• Resolved Spectroscopy of Gravitationally-Lensed Galaxies: Recovering Coherent Velocity Fields in Sub-Luminous z~2-3 Galaxies
• Finding Strong Gravitational Lenses in the Kilo Degree Survey with Convolutional Neural Networks
• 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 CFHTLS-Strong Lensing Legacy Survey (SL2S): Investigating the group-scale lenses with the SARCS sample
• Gravitational Lens Time Delays: A Statistical Assessment of Lens Model Dependences and Implications for the Global Hubble Constant
• The Strong Gravitational Lens Finding Challenge
• The Spatial Structure of An Accretion Disk
• Finding strong lenses in CFHTLS using convolutional neural networks
• The Emission Line Properties of Gravitationally-lensed 1.5 < z < 5 Galaxies
• The Incidence of Strong-Lensing Clusters in the Red-Sequence Cluster Survey
• Modelling the line-of-sight contribution in substructure lensing
• 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
• 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
• 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
• Substructure in lensing clusters and simulations
• Identifying Strong Lenses with Unsupervised Machine Learning using Convolutional Autoencoder
• GLAMER Part I: A Code for Gravitational Lensing Simulations with Adaptive Mesh Refinement
• Probing the nature of dark matter by forward modeling flux ratios in strong gravitational lenses
• GLAMER Part II: Multiple Plane Gravitational Lensing
• The Millennium Run Observatory: First Light
• A Systematic Search for High Surface Brightness Giant Arcs in a Sloan Digital Sky Survey Cluster Sample
• A PCA-based automated finder for galaxy-scale strong lenses
• Testing Convolutional Neural Networks for finding strong gravitational lenses in KiDS
• Broadband Photometry of 105 Giant Arcs: Redshift Constraints and Implications for Giant Arc Statistics
• KiDS-SQuaD: The KiDS Strongly lensed Quasar Detection project
• Gravitational lensing as a probe of cold dark matter subhalos
• 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 Discovery of a Five-Image Lensed Quasar at z = 3.34 using PanSTARRS1 and Gaia
• A Neural Network Gravitational Arc Finder based on the Mediatrix filamentation Method
• A candidate gravitational lens in the Hubble Deep Field
• Strong Gravitational Lensing by the Super-massive cD Galaxy in Abell 3827
• A seven square degrees survey for galaxy-scale gravitational lenses with the HST imaging archive
• Strong lens modelling: comparing and combining Bayesian neural networks and parametric profile fitting
• Detecting gravitational lenses using machine learning: exploring interpretability and sensitivity to rare lensing configurations
• A method to search for strong galaxy-galaxy lenses in optical imaging surveys
• REQUIEM-2D: Spatially Resolved Stellar Populations from HST 2D Grism Spectroscopy
• Feasibility of Probing Dark Energy with Strong Gravitational Lensing Systems -- Fisher-Matrix Approach --
• Deep Learning Assessment of galaxy morphology in S-PLUS DataRelease 1
• DeepZipper: A Novel Deep Learning Architecture for Lensed Supernovae Identification
• A Comparative Study of Convolutional Neural Networks for the Detection of Strong Gravitational Lensing
• Deep Learning Blazar Classification based on Multi-frequency Spectral Energy Distribution Data

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• 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
• Transient Classifiers for Fink: Benchmarks for LSST
• Strong Gravitational Lensing in Horndeski theory
• HOLISMOKES XV. Search for strong gravitational lenses combining ground-based and space-based imaging