Weak lensing shear estimation beyond the shape-noise limit: a machine
  learning approach
  Weak lensing shear estimation typically results in per galaxy statistical
errors significantly larger than the sought after gravitational signal of only
a few percent. These statistical errors are mostly a result of shape-noise --
an estimation error due to the diverse (and a-priori unknown) morphology of
individual background galaxies. These errors are inversely proportional to the
limiting angular resolution at which localized objects, such as galaxy
clusters, can be probed with weak lensing shear. In this work we report on our
initial attempt to reduce statistical errors in weak lensing shear estimation
using a machine learning approach -- training a multi-layered convolutional
neural network to directly estimate the shear given an observed background
galaxy image. We train, calibrate and evaluate the performance and stability of
our estimator using simulated galaxy images designed to mimic the distribution
of HST observations of lensed background sources in the CLASH galaxy cluster
survey. Using the trained estimator, we produce weak lensing shear maps of the
cores of 20 galaxy clusters in the CLASH survey, demonstrating an RMS scatter
reduced by approximately 26% when compared to maps produced with a commonly
used shape estimator. This is equivalent to a survey speed enhancement of
approximately 60%. However, given the non-transparent nature of the machine
learning approach, this result requires further testing and validation. We
provide python code to train and test this estimator on both simulated and real
galaxy cluster observations. We also provide updated weak lensing catalogues
for the 20 CLASH galaxy clusters studied.
