Prediction of galaxy ellipticities and reduction of shape noise in cosmic shear measurements

TL;DR

This study demonstrates that galaxy ellipticities can be predicted from photometric data alone, significantly reducing shape noise in weak lensing measurements and improving shear estimation precision.

Contribution

It introduces the use of regression models to predict galaxy ellipticities from photometric parameters, enhancing weak lensing analysis without additional costly observations.

Findings

Predicting ellipticities improves shear measurement precision.

Achieved a gain equivalent to 114.3% more galaxies using PPR.

Using lensing-unaffected parameters yields a 12% gain.

Abstract

The intrinsic scatter in the ellipticities of galaxies about the mean shape, known as "shape noise," is the most important source of noise in weak lensing shear measurements. Several approaches to reducing shape noise have recently been put forward, using information beyond photometry, such as radio polarization and optical spectroscopy. Here we investigate how well the intrinsic ellipticities of galaxies can be predicted using other, exclusively photometric parameters. These parameters (such as galaxy colours) are already available in the data and do not necessitate additional, often expensive observations. We apply two regression techniques, generalized additive models (GAM) and projection pursuit regression (PPR) to the publicly released data catalog of galaxy properties from CFHTLenS. In our simple analysis we find that the individual galaxy ellipticities can indeed be predicted from other photometric parameters to better precision than the scatter about the mean ellipticity. This means that without additional observations beyond photometry the ellipticity contribution to the shear can be measured to higher precision, comparable to using a larger sample of galaxies. Our best-fit model, achieved using PPR, yields a gain equivalent to having 114.3% more galaxies. Using only parameters unaffected by lensing (e.g.~surface brightness, colour), the gain is only ~12%.