On the effect of image denoising on galaxy shape measurements

TL;DR

This paper investigates how image denoising techniques can enhance galaxy shape measurements crucial for weak gravitational lensing studies, demonstrating improved accuracy and galaxy detection using synthetic data and a new wavelet-based method.

Contribution

It introduces a novel wavelet-based denoising method that significantly improves shape measurement quality in simulated weak lensing data.

Findings

Denoising improves shape measurement accuracy.

The new wavelet-based method doubles the quality factor in tests.

Denoising enables better galaxy detection for surveys like EUCLID.

Abstract

Weak gravitational lensing is a very sensitive way of measuring cosmological parameters, including dark energy, and of testing current theories of gravitation. In practice, this requires exquisite measurement of the shapes of billions of galaxies over large areas of the sky, as may be obtained with the EUCLID and WFIRST satellites. For a given survey depth, applying image denoising to the data both improves the accuracy of the shape measurements and increases the number density of galaxies with a measurable shape. We perform simple tests of three different denoising techniques, using synthetic data. We propose a new and simple denoising method, based on wavelet decomposition of the data and a Wiener filtering of the resulting wavelet coefficients. When applied to the GREAT08 challenge dataset, this technique allows us to improve the quality factor of the measurement (Q; GREAT08 definition), by up to a factor of two. We demonstrate that the typical pixel size of the EUCLID optical channel will allow us to use image denoising.