Reducing distance errors for standard candles and standard sirens with weak-lensing shear and flexion maps

TL;DR

This paper explores how weak-lensing shear and flexion maps can be used to correct distance errors in high-redshift standard candles and sirens, improving cosmological measurements.

Contribution

It demonstrates optimal filtering methods for convergence maps from shear and flexion data to significantly reduce lensing-induced distance errors.

Findings

A shear survey with 100 galaxies/arcmin^2 reduces errors by 20% at z=1.5.

A survey with 500 galaxies/arcmin^2 reduces errors by 50% at z=1.5.

Using galaxy redshifts in reconstruction improves error reduction by 5% at z>=3.

Abstract

Gravitational lensing induces significant errors in the measured distances to high-redshift standard candles and standard sirens such as type-Ia supernovae, gamma-ray bursts, and merging supermassive black hole binaries. There will therefore be a significant benefit from correcting for the lensing error by using independent and accurate estimates of the lensing magnification. We investigate how accurately the magnification can be inferred from convergence maps reconstructed from galaxy shear and flexion data. We employ ray-tracing through the Millennium Simulation to simulate lensing observations in large fields, and perform a weak-lensing reconstruction on these fields. We identify optimal ways to filter the reconstructed convergence maps and to convert them to magnification maps. We find that a shear survey with 100 galaxies/arcmin^2 can help to reduce the lensing-induced distance errors for standard candles/sirens at redshifts z=1.5 (z=5) on average by 20% (10%), whereas a futuristic survey with shear and flexion estimates from 500 galaxies/arcmin^2 yields much larger reductions of 50% (35%). For redshifts z>=3, a further improvement by 5% can be achieved, if the individual redshifts of the galaxies are used in the reconstruction. Moreover, the reconstruction allows one to identify regions for which the convergence is low, and in which an error reduction by up to 75% can be achieved.