Bias-Free Shear Estimation using Artificial Neural Networks

Abstract

Bias due to imperfect shear calibration is the biggest obstacle when constraints on cosmological parameters are to be extracted from large area weak lensing surveys such as Pan-STARRS-3pi, DES or future satellite missions like Euclid. We demonstrate that bias present in existing shear measurement pipelines (e.g. KSB) can be almost entirely removed by means of neural networks. In this way, bias correction can depend on the properties of the individual galaxy instead on being a single global value. We present a procedure to train neural networks for shear estimation and apply this to subsets of simulated GREAT08 RealNoise data. We also show that circularization of the PSF before measuring the shear reduces the scatter related to the PSF anisotropy correction and thus leads to improved measurements, particularly on low and medium signal-to-noise data. Our results are competitive with the best performers in the GREAT08 competition, especially for the medium and higher signal-to-noise sets. Expressed in terms of the quality parameter defined by GREAT08 we achieve a Q = 40, 140 and 1300 without and 50, 200 and 1300 with circularization for low, medium and high signal-to-noise data sets, respectively.

19 pages, 8 figures; accepted for publication in ApJ

References in corpus (5)

• The Shear TEsting Programme 2: Factors affecting high precision weak lensing analyses
• Evidence for the accelerated expansion of the Universe from weak lensing tomography with COSMOS
• Systematic Bias in Cosmic Shear: Beyond the Fisher Matrix
• Results of the GREAT08 Challenge: An image analysis competition for cosmological lensing
• Handbook for the GREAT08 Challenge: An image analysis competition for cosmological lensing

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• Dark matter halo properties from galaxy-galaxy lensing
• Gravitational Lensing Accuracy Testing 2010 (GREAT10) Challenge Handbook
• Weak-lensing shear measurement with machine learning: teaching artificial neural networks about feature noise
• Accounting for object detection bias in weak gravitational lensing studies
• Decoding Dark Matter Substructure without Supervision
• A fast empirical method for galaxy shape measurements in weak lensing surveys
• Deep learning the astrometric signature of dark matter substructure
• Forklens: Accurate weak-lensing shear measurement with deep learning
• Hopfield Neural Network deconvolution for weak lensing measurement
• Shear measurement bias II: a fast machine learning calibration method
• Impact of PSF misestimation and galaxy population bias on precision shear measurement using a CNN