Mass Reconstruction of Galaxy-scale Strong Gravitational Lenses Using a Broken Power-law Model
Wei Du, Liping Fu, Yiping Shu, Ran Li, Zuhui Fan, Chenggang Shu

TL;DR
This study evaluates the broken power-law (BPL) model's effectiveness in reconstructing galaxy-scale strong lensing masses using mock data, demonstrating its advantages over simpler models in accuracy and robustness.
Contribution
The paper provides an end-to-end assessment of the BPL model's performance in mass reconstruction, showing its superiority over single power-law models with robust bias metrics.
Findings
Einstein radius can be accurately determined with less than 1% bias.
BPL model achieves under 5% median bias within the Einstein radius.
BPL model estimates velocity dispersions with approximately 6% scatter.
Abstract
With mock strong gravitational lensing images, we investigate the performance of the broken power-law (BPL) model proposed by \citet{2020ApJ...892...62D} on the mass reconstruction of galaxy-scale lenses. An end-to-end test is carried out, including the creation of mock strong lensing images, the subtraction of lens light, and the reconstruction of lensed images, where the lenses are selected from the galaxies in the Illustris-1 simulation. We notice that, regardless of the adopted mass models (the BPL model or its special cases), the Einstein radius can be robustly determined from imaging data alone, and the median bias is typically less than . Away from the Einstein radius, the lens mass distribution tends to be harder to measure, especially at radii where there are no lensed images detected. We find that, with rigid priors, the BPL model can clearly outperform the single…
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Taxonomy
TopicsGalaxies: Formation, Evolution, Phenomena · Adaptive optics and wavefront sensing · Stellar, planetary, and galactic studies
