Image segmentation for analyzing galaxy-galaxy strong lensing systems

Understand

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.