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