Weak lensing shear estimation beyond the shape-noise limit: a machine learning approach

Paper abstract -

Weak lensing shear estimation typically results in per galaxy statistical errors significantly larger than the 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.

Dataset abstract -

This dataset contains simulated galaxy stamps of high redshift galaxies, having known weak lensing shear. These were used to train and test the shear estimators described in the paper. The simulations were performed using the GalSim package (https://github.com/GalSim-developers/GalSim) and were designed to match background galaxies in the HST galaxy cluster observations made available by the CLASH collaboration (https://archive.stsci.edu/prepds/clash). Refer to the following two Github repositories for documentation about how this dataset is structured (https://github.com/ofersp/wlenet, https://github.com/ofersp/wlenet-data).