Weak lensing shear estimation typically results in per galaxy statistical errors significantly larger than the sought after gravitational signal of only a few per cent. 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 multilayered 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 per cent when compared to maps produced with a commonly used shape estimator. This is equivalent to a survey speed enhancement of approximately 60 per cent. 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.
Bibliographical noteFunding Information:
We thank Anton Koekemoer, Rachel Mandelbaum, Elinor Medezinski, Keichi Umetsu, Adi Zitrin, and Boaz Nadler for fruitful discussions. EOO is grateful for support by a grant from the Israeli Ministry of Science, Minerva, Binational Science Foundation (BSF), BSF transformative program, and the Israeli Centers for Research Excellence (I-CORE) Program of the Planning and Budgeting Committee and The Israel Science Foundation (ISF) (grant No 1829/12). OMS, EOO, and YW are grateful for support by a grant from the ISF. JM has received funding from the European Union’s Horizon 2020 research and Innovation programme under the Marie Skłodowska-Curie grant agreement No 664931. We thank the anonymous reviewer whose comments helped us improve the manuscript.
© 2019 The Author(s).
- Galaxies: clusters: general
- Gravitational lensing: weak
- Methods: statistical