Self-calibration of weak lensing cosmic shear biases

TL;DR

This paper presents a new method for calibrating weak lensing cosmic shear biases that estimates multiplicative and additive biases directly from data without external simulations, improving accuracy for future surveys.

Contribution

It introduces a joint bias inference technique that marginalizes over hyper-parameters, reducing reliance on simulations and independent of cosmological assumptions.

Findings

Effective bias estimation even with noisy data

Reduces dependence on external simulations

Applicable to current and upcoming lensing surveys

Abstract

In order to reach the required performance of Stage-III and IV weak lensing surveys, cosmic shear measurements have to rely on external simulations to calibrate residual biases. Over the years, several techniques have been developed to mitigate the impact of residual biases prior to calibration, including the inference of shear responses on images to correct multiplicative biases, and the empirical correction of additive biases. We introduce a novel methodology that generalises upon the state-of-the-art approaches by inferring multiplicative and additive biases jointly from parameterised distributions of measured ellipticities, crucially without relying on external simulations and independently from cosmology. Shear biases are marginalised over the unknown hyper-parameters in the modelling, hence mitigating the impact of degeneracies. We apply the technique to a representative problem and show the performance of the estimation, even in the presence of noise. The method has a high potential for applicability to the calibration of weak lensing cosmic shear in current and future lensing surveys.