Metacalibration: Direct Self-Calibration of Biases in Shear Measurement

TL;DR

Metacalibration is a novel, data-driven method for directly calibrating shear measurement biases in weak lensing surveys, reducing reliance on simulations and improving accuracy.

Contribution

We introduce a general, statistically principled technique that infers shear calibration parameters by modifying survey data, applicable to all current shear estimation methods.

Findings

Eliminates calibration biases in simulated data

Achieves sub-percent calibration accuracy

Applicable to various shear measurement techniques

Abstract

One of the primary limiting sources of systematic uncertainty in forthcoming weak lensing measurements is systematic uncertainty in the quantitative relationship between the distortions due to gravitational lensing and the measurable properties of galaxy images. We present a statistically principled, general solution to this problem. Our technique infers multiplicative shear calibration parameters by modifying the actual survey data to simulate the effects of a known shear. It can be applied to any shear estimation method based on weighted averages of galaxy shape measurements, which includes all methods used to date for shear estimation with real data. Use of the real images mitigates uncertainty due to unknown galaxy morphology, which is a serious concern for calibration of shear estimates based on image simulations. We test our results on simulated images from the GREAT3 challenge, and show that the method eliminates calibration biases for several different shape measurement techniques at the level of precision measurable with the GREAT3 simulations (a few tenths of a percent).