Impact of Atmospheric Chromatic Effects on Weak Lensing Measurements

TL;DR

This paper investigates how atmospheric chromatic effects, like differential refraction and wavelength-dependent seeing, bias galaxy shape measurements in weak lensing surveys, and proposes machine-learning based correction methods.

Contribution

It identifies significant atmospheric chromatic biases in galaxy shape measurements and introduces a machine-learning correction approach to mitigate these biases.

Findings

Chromatic effects cause measurable biases in galaxy shapes.

Machine-learning corrections can effectively reduce biases.

Wavelength dependence of the PSF is crucial for precision weak lensing.

Abstract

Current and future imaging surveys will measure cosmic shear with statistical precision that demands a deeper understanding of potential systematic biases in galaxy shape measurements than has been achieved to date. We use analytic and computational techniques to study the impact on shape measurements of two atmospheric chromatic effects for ground-based surveys such as the Dark Energy Survey and the Large Synoptic Survey Telescope (LSST): (i) atmospheric differential chromatic refraction and (ii) wavelength dependence of seeing. We investigate the effects of using the point spread function (PSF) measured with stars to determine the shapes of galaxies that have different spectral energy distributions than the stars. We find that both chromatic effects lead to significant biases in galaxy shape measurements for current and future surveys, if not corrected. Using simulated galaxy images, we find a form of chromatic `model bias' that arises when fitting a galaxy image with a model that has been convolved with a stellar, instead of galactic, point spread function. We show that both forms of atmospheric chromatic biases can be predicted (and corrected) with minimal model bias by applying an ordered set of perturbative PSF-level corrections based on machine-learning techniques applied to six-band photometry. Catalog-level corrections do not address the model bias. We conclude that achieving the ultimate precision for weak lensing from current and future ground-based imaging surveys requires a detailed understanding of the wavelength dependence of the PSF from the atmosphere, and from other sources such as optics and sensors. The source code for this analysis is available at https://github.com/DarkEnergyScienceCollaboration/chroma .