Statistical Strong Lensing V. Inference with Microlensed Quasars
astro-ph.GA
Submitted: 2026-10-17
Updated: 2026-10-17
Comments: 12 pages, 5 figures. Submitted to Astronomy & Astrophysics
License: http://creativecommons.org/licenses/by/4.0/
The gist: Microlensing of strongly lensed quasars provides direct information on the stellar component of lens galaxies, and can help break the degeneracy between the stellar initial mass function (IMF) and
Abstract
Microlensing of strongly lensed quasars provides direct information on the stellar component of lens galaxies, and can help break the degeneracy between the stellar initial mass function (IMF) and the dark matter. However, a statistical framework to forward model microlensing data while accounting for strong lensing selection effects is still missing. We incorporated microlensing effects into the hierarchical model of Paper III, and investigated to what extent flux measurements of strongly lensed quasars can break the degeneracy between the stellar IMF and the dark matter distribution. We tested the method on a mock sample of 219 doubly imaged quasars, lensed by circularly symmetric lenses. When microlensing effects are modelled, the inference using image fluxes recovers the input foreground population parameters and provides tighter constraints than the inference without flux constraints. The improvement is clearest in the μ α-μ 5 plane, where μ α describes the mean stellar population synthesis mismatch parameter α SPS and μ 5 describes the mean projected dark matter mass within 5, kpc. The 68% credible interval on μ α shrinks from 0.16 dex to 0.09 dex, with the degeneracy between μ α and μ 5 significantly reduced. By contrast, fitting the image fluxes without accounting for microlensing leads to large biases on the inferred parameters. This work is a first step towards an accurate modelling of microlensing data. Application to real lens samples will require dropping the circular symmetry assumption and adopting more realistic models for the microlensing magnification.
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