Strong Gravitational Lens Statistics using the Herschel-ATLAS

TL;DR

This paper analyzes the statistical properties of strong gravitational lenses identified in the Herschel-ATLAS survey, demonstrating the potential for cosmological and astrophysical insights using submillimetre wavelength data.

Contribution

It tests analytical models of lens statistics against Herschel-ATLAS data, exploring the implications of different density profiles and cosmological parameters.

Findings

SIS density profile is preferred by current data

Herschel-ATLAS lenses can constrain cosmological parameters

Large lens sample can improve understanding of astrophysical processes

Abstract

The identification of strong gravitational lenses in large surveys has historically been a rather time consuming exercise. Early data from the Herschel Astrophysical Terahertz Large Area Survey (Herschel-ATLAS) demonstrate that lenses can be identified efficiently at submillimetre wavelengths using a simple flux criteria. Motivated by that development, this work considers the statistical properties of strong gravitational lens systems which have been, and will be, found by the Herschel-ATLAS. Analytical models of lens statistics are tested with the current best estimates for the various model ingredients. These include the cosmological parameters, the mass function and the lens density profile, for which we consider the singular isothermal sphere (SIS) and the Navarro, Frenk & White (NFW) approximations. The five lenses identified in the Herschel-ATLAS Science Demonstration Phase suggest a SIS density profile is preferred, but cannot yet constrain \Omega_\Lambda to an accuracy comparable with other methods. The complete Herschel-ATLAS data set should be sufficient to yield competitive constraints on \Omega_\Lambda. Whilst this huge number of lenses has great potential for constraining cosmological parameters, they will be most powerful in constraining uncertainty in astrophysical processes. Further investigation is needed to fully exploit this unprecedented data set.