A fast empirical method for galaxy shape measurements in weak lensing surveys

TL;DR

This paper introduces a rapid, supervised learning-based method for correcting galaxy ellipticity measurements affected by PSF distortions, enhancing weak lensing analysis efficiency and accuracy.

Contribution

It presents a novel, fast correction technique using lookup tables and supervised learning, achieving high-quality results without extensive training or denoising.

Findings

Achieved a quality factor of Q=104 on the GREAT10 challenge.

Processed each galaxy in under 3 milliseconds on standard CPUs.

Method outperforms traditional approaches in speed and competitive accuracy.

Abstract

We describe a simple and fast method to correct ellipticity measurements of galaxies from the distortion by the instrumental and atmospheric point spread function (PSF), in view of weak lensing shear measurements. The method performs a classification of galaxies and associated PSFs according to measured shape parameters, and corrects the measured galaxy ellipticites by querying a large lookup table (LUT), built by supervised learning. We have applied this new method to the GREAT10 image analysis challenge, and present in this paper a refined solution that obtains the competitive quality factor of Q = 104, without any shear power spectrum denoising or training. Of particular interest is the efficiency of the method, with a processing time below 3 ms per galaxy on an ordinary CPU.