A Robust Mass Estimator for Dark Matter Subhalo Perturbations in Strong Gravitational Lenses
Year: 2017
Type: article
Abstract: A few dark matter substructures have recently been detected in strong gravitational lenses through their perturbations of highly magnified images. We derive a characteristic scale for lensing perturbations and show that they are significantly larger than the perturber’s Einstein radius. We show that the perturber’s projected mass enclosed within this radius, scaled by the log-slope of the host galaxy’s density profile, can be robustly inferred even if the inferred density profile and tidal radius of the perturber are biased. We demonstrate the validity of our analytic derivation using several gravitational lens simulations where the tidal radii and the inner log-slopes of the density profile of the perturbing subhalo are allowed to vary. By modeling these simulated data, we find that our mass estimator, which we call the effective subhalo lensing mass, is accurate to within about 10% or smaller in each case, whereas the inferred total subhalo mass can potentially be biased by nearly an order of magnitude. We therefore recommend that the effective subhalo lensing mass be reported in future lensing reconstructions, as this will allow for a more accurate comparison with the results of dark matter simulations.
Source: The Astrophysical Journal
Authors: Quinn E. Minor, Manoj Kaplinghat, Nan Li
Language: English
Subfield: Astronomy and Astrophysics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: bronze
Awards: G102163711, G107546177, G1313983767, II-EN: CUNY Computing Research Infrastructure, G1664757510, G1846807563, G262962599, G3075337988, Testing the Cold Dark Matter Model by Constraining Dark Matter Substructure in Gravitational Lens Galaxies, Particle Physics and Cosmology in the LHC Era, MRI: Instrumentation for Enabling Data Analysis, Sharing, Storage, and Preservation, G513797252, G5573261709, II-EN: City University of New York - Computing Research Infrastructure, G9596489
OpenAlex ID: W2562374416
