Accounting for object detection bias in weak gravitational lensing studies

TL;DR

This paper investigates how object detection biases, especially blending, affect weak lensing measurements in large surveys, and demonstrates that accounting for these biases can meet future survey requirements.

Contribution

It highlights the significance of detection biases in weak lensing and shows that MetaDetection can mitigate these biases if the PSF is accurately modeled.

Findings

Detection bias can exceed survey requirements without correction.

MetaDetection effectively accounts for blending biases.

Accurate PSF modeling is crucial for bias mitigation.

Abstract

Weak lensing by large-scale structure is a powerful probe of cosmology if the apparent alignments in the shapes of distant galaxies can be accurately measured. Most studies have therefore focused on improving the fidelity of the shape measurements themselves, but the preceding step of object detection has been largely ignored. In this paper we study the impact of object detection for a Euclid-like survey and show that it leads to biases that exceed requirements for the next generation of cosmic shear surveys. In realistic scenarios, blending of galaxies is an important source of detection bias. We find that MetaDetection is able to account for blending, leading to average multiplicative biases that meet requirements for Stage IV surveys, provided a sufficiently accurate model for the point spread function is available. Further work is needed to estimate the performance for actual surveys. Combined with sufficiently realistic image simulations, this provides a viable way forward towards accurate shear estimates for Stage IV surveys.