Macromodel-free flux-ratio prediction in quadruply imaged quasars with local constraints from lensed arcs

Hadrien Paugnat, Tommaso Treu, Daniel Gilman

公開日: 2025/9/5

Abstract

Strong gravitational lensing is a powerful cosmological probe, providing a direct tool to unveil the properties of dark matter (DM) on sub-galactic scales. In particular, flux-ratio anomalies in quadruply imaged quasars ("quads") can reveal the presence of dark substructure, such that population-level statistics can be used to constrain the particle nature of DM. Current methods, however, rely on globally parametrized models ("macromodels") of the lens mass distribution, which impose rigid physical assumptions on the deflection field. Given the high stakes, it is important to develop complementary methods that do not require the assumption of a macromodel. One promising avenue consists of modeling the resolved emission from the quasar host galaxy (lensed arcs) using a local lensing formalism like the Curved Arc Basis (CAB) description. In this paper, we test the ability of CAB models to predict flux ratios from mock imaging data. We find that CAB model-predicted flux ratios accurately reproduce the expected values, with a typical precision of $\sim 3-5\%$. While a macromodel-based approach yields smaller uncertainties, as expected, the CAB method permits a more flexible, local description of the deflection field, thus being more robust to angular structure in the main deflector mass profile, in particular avoiding false-positive detections of flux-ratio anomalies that can arise with overly simplistic parametrizations. On the other hand, by injecting individual DM halos near quasar images, we demonstrate that CAB models do not absorb the local lensing perturbations from DM substructure, and can therefore distinguish flux-ratio anomalies caused by DM substructure from other sources of small-scale perturbation. We conclude that CAB models can be used to infer DM properties from flux-ratio anomaly statistics with minimal assumptions, complementing the traditional macromodel based approach.