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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 lensingshear measurements. Several approaches to reducing shape noise have recentlybeen put forward, using information beyond photometry, such as radiopolarization and optical spectroscopy. Here we investigate how well theintrinsic ellipticities of galaxies can be predicted using other, exclusivelyphotometric parameters. These parameters (such as galaxy colours) are alreadyavailable in the data and do not necessitate additional, often expensiveobservations. We apply two regression techniques, generalized additive models(GAM) and projection pursuit regression (PPR) to the publicly released datacatalog of galaxy properties from CFHTLenS. In our simple analysis we find thatthe individual galaxy ellipticities can indeed be predicted from otherphotometric parameters to better precision than the scatter about the meanellipticity. This means that without additional observations beyond photometrythe ellipticity contribution to the shear can be measured to higher precision,comparable to using a larger sample of galaxies. Our best-fit model, achievedusing PPR, yields a gain equivalent to having 114.3% more galaxies. Using onlyparameters unaffected by lensing (e.g.~surface brightness, colour), the gain isonly ~12%.

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