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.

v1: 5 + 3 pages, 3 figures. v2: matches accepted version in A&A Letters. 5+2 pages; 3+1 figures

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• 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