Lens Modeling and Cosmological Inference from an Impure Sample of Galaxy-Galaxy Strong Lenses

Abstract The start of the Legacy Survey of Space and Time marks a new era for strong lensing science, where the number of strong lenses identified is expected to increase to ∼100 000. In this paper we use a neural network to determine the precision with which lens parameters can be determined, using realistic simulated LSST lensed systems. We find that the Einstein radius can be measured with a mean precision of 3.7% with calibrated uncertainties accurately reflecting the corresponding measurement error. Based on the performance of current strong lens classifiers, the ∼100, 000 detectable strong lenses are expected to be accompanied by a similar or larger number of false positives (non-lenses). In readiness for this we introduce a formalism, termed ‘COSMIC-BEAMS’, to infer cosmological parameters while accounting for contamination by false positives. As a proof-of-concept, using simulated LSST measurements of the Einstein radii of a realistic and impure sample of photometric lens systems, i.e. those without spectroscopic confirmation, we find that the cosmological parameters Ωm, $\Omega _\Lambda$, and w can be measured to a precision of 0.1, 0.03 and 0.15 respectively for a wCDM cosmology. We demonstrate that unbiased cosmological parameters can be inferred even in strong lens samples contaminated by 50% false positives, and that the photometric dataset of 100 000 strong lenses will provide equivalent w-precision to 2500 − 3500 spectroscopic systems.