Statistical strong lensing. II. Cosmology and galaxy structure with time-delay lenses
Abstract
Context. Time delay lensing is a powerful tool to measure the Hubble constant . In order to obtain an accurate estimate of from a sample of time delay strong lenses, however, it is necessary to have a very good knowledge of the mass structure of the lens galaxies. Strong lensing data on their own are not sufficient to break the degeneracy between and the lens model parameters, on a single object basis. Aims. The goal of this study is to determine whether it is possible to break the -lens structure degeneracy with the statistical combination of a large sample of time-delay lenses, relying purely on strong lensing data (that is, with no stellar kinematics information). Methods. I simulated a set of 100 lenses with doubly imaged quasars and related time delay measurements. I fitted these data with a Bayesian hierarchical method and a flexible model for the lens population, emulating the lens modelling step. Results. The sample of 100 lenses, on its own, provides a measurement of with precision, but with a bias. However, the addition of prior information on the lens structural parameters from a large sample of lenses with no time delays, such as that considered in Paper I, allows for a -level inference. Moreover, the 100 lenses allow for a ~dex calibration of galaxy stellar masses, regardless of the level of prior knowledge of the Hubble constant. Conclusions. Breaking the -lens model degeneracy with lensing data alone is possible, but measurements of require either a number of time delay lenses much larger than 100, or the knowledge of the structural parameter distribution of the lens population from a separate sample of lenses.
Keywords
Cite
@article{arxiv.2109.00009,
title = {Statistical strong lensing. II. Cosmology and galaxy structure with time-delay lenses},
author = {Alessandro Sonnenfeld},
journal= {arXiv preprint arXiv:2109.00009},
year = {2021}
}
Comments
Published on Astronomy & Astrophysics. A 2-minute summary video is available at https://youtu.be/EBiWvKCL6yk