Image segmentation for analyzing galaxy-galaxy strong lensing systems

Abstract

The goal of this paper is to develop a machine learning model to analyze the main gravitational lens and detect dark substructure (subhalos) within simulated images of strongly lensed galaxies. Using the technique of image segmentation, we turn the task of identifying subhalos into a classification problem, where we label each pixel in an image as coming from the main lens, a subhalo within a binned mass range, or neither. Our network is only trained on images with a single smooth lens and either zero or one subhalo near the Einstein ring. On an independent test set with lenses with large ellipticities, quadrupole and octopole moments, and for source apparent magnitudes between 17-25, the area of the main lens is recovered accurately. On average, only 1.3% of the true area is missed and 1.2% of the true area is added to another part of the lens. In addition, subhalos as light as $10^{8.5}{M}_{\odot }$ can be detected if they lie in bright pixels along the Einstein ring. Furthermore, the model is able to generalize to new contexts it has not been trained on, such as locating multiple subhalos with varying masses or more than one large smooth lens.

arXiv commentv1: 5 + 3 pages, 3 figures. v2: matches accepted version in A&A Letters. 5+2 pages; 3+1 figures

References in corpus (26)

• Planck 2015 results. XIII. Cosmological parameters
• The Structure of Cold Dark Matter Halos
• Deep Visual Domain Adaptation: A Survey
• The Aquarius Project: the subhalos of galactic halos
• Evidence for substructure in lens galaxies?
• Gravitational detection of a low-mass dark satellite at cosmological distance
• Lenstronomy: multi-purpose gravitational lens modelling software package
• Detection of a Dark Substructure through Gravitational Imaging
• Bayesian Strong Gravitational-Lens Modeling on Adaptive Grids: Objective Detection of Mass Substructure in Galaxies
• The Hubble Space Telescope Wide Field Camera 3 Early Release Science data: Panchromatic Faint Object Counts for 0.2-2 microns wavelength
• TDCOSMO. I. An exploration of systematic uncertainties in the inference of $H_0$ from time-delay cosmography
• Inference of the Cold Dark Matter substructure mass function at z=0.2 using strong gravitational lenses
• Analytic models of plausible gravitational lens potentials
• Gravitational lens modeling with basis sets
• Detecting dark matter substructure spectroscopically in strong gravitational lenses
• Modelling the line-of-sight contribution in substructure lensing
• Low-mass halo perturbations in strong gravitational lenses at redshift z$\sim$0.5 are consistent with CDM
• Quantifying Environmental and Line-of-Sight Effects in Models of Strong Gravitational Lens Systems
• Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning
• Dark Matter Substructure Detection Using Spatially Resolved Spectroscopy of Lensed Dusty Galaxies
• Projection effects in the strong lensing study of subhaloes
• Cosmography with cluster strong lenses: the influence of substructure and line-of-sight halos
• Deep Learning the Morphology of Dark Matter Substructure
• Direct Detection of Dark Matter Substructure in Strong Lens Images with Convolutional Neural Networks
• Probing the small-scale structure in strongly lensed systems via transdimensional inference
• Quantifying the Line-of-Sight Halo Contribution to the Dark Matter Convergence Power Spectrum from Strong Gravitational Lenses

Cited by in corpus (15)

• From Images to Dark Matter: End-To-End Inference of Substructure From Hundreds of Strong Gravitational Lenses
• Sensitivity of strong lensing observations to dark matter substructure: a case study with Euclid
• Using wavelets to capture deviations from smoothness in galaxy-scale strong lenses
• Strong Lensing by Galaxies
• Extracting the Subhalo Mass Function from Strong Lens Images with Image Segmentation
• Astrophysical Tests of Dark Matter Self-Interactions
• One never walks alone: the effect of the perturber population on subhalo measurements in strong gravitational lenses
• Machine Learning and Cosmology
• Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
• Subhalo effective density slope measurements from HST strong lensing data with neural likelihood-ratio estimation
• Inferring subhalo effective density slopes from strong lensing observations with neural likelihood-ratio estimation
• Anisotropic strong lensing as a probe of dark matter self-interactions
• Deep learning approach for identification of HII regions during reionization in 21-cm observations -- III. image recovery
• Godzilla, a monster lurks in the Sunburst galaxy
• Probing Dark Matter with Strong Gravitational Lensing through an Effective Density Slope