Image segmentation for analyzing galaxy-galaxy strong lensing systems

TL;DR

This paper develops a machine learning image segmentation model to analyze gravitational lensing images, accurately identifying the main lens and subhalos, including small dark matter substructures, with high precision and generalization.

Contribution

The study introduces a novel image segmentation approach for gravitational lens analysis that generalizes well to complex lensing scenarios and detects small subhalos.

Findings

Main lens area recovered with only 1.3% missed

Subhalos as light as 10^{8.5}M_{\odot} detected

Model generalizes to multiple subhalos and complex lenses

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.