TY - JOUR
T1 - Weak lensing shear estimation beyond the shape-noise limit
T2 - A machine learning approach
AU - Springer, Ofer M.
AU - Ofek, Eran O.
AU - Weiss, Yair
AU - Merten, Julian
N1 - Publisher Copyright:
© 2019 The Author(s).
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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.
AB - 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.
KW - Galaxies: clusters: general
KW - Gravitational lensing: weak
KW - Methods: statistical
UR - http://www.scopus.com/inward/record.url?scp=85091229529&partnerID=8YFLogxK
U2 - 10.1093/mnras/stz2991
DO - 10.1093/mnras/stz2991
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AN - SCOPUS:85091229529
SN - 0035-8711
VL - 491
SP - 5301
EP - 5316
JO - Monthly Notices of the Royal Astronomical Society
JF - Monthly Notices of the Royal Astronomical Society
IS - 4
ER -