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• W3151390452 endingPage "1839" @default.
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• W3151390452 abstract "ABSTRACT We propose a deep-learning approach based on generative adversarial networks (GANs) to reduce noise in weak lensing mass maps under realistic conditions. We apply image-to-image translation using conditional GANs to the mass map obtained from the first-year data of Subaru Hyper Suprime-Cam (HSC) Survey. We train the conditional GANs by using 25 000 mock HSC catalogues that directly incorporate a variety of observational effects. We study the non-Gaussian information in denoised maps using one-point probability distribution functions (PDFs) and also perform matching analysis for positive peaks and massive clusters. An ensemble learning technique with our GANs is successfully applied to reproduce the PDFs of the lensing convergence. About $60{{ rm per cent}}$ of the peaks in the denoised maps with height greater than 5σ have counterparts of massive clusters within a separation of 6 arcmin. We show that PDFs in the denoised maps are not compromised by details of multiplicative biases and photometric redshift distributions, nor by shape measurement errors, and that the PDFs show stronger cosmological dependence compared to the noisy counterpart. We apply our denoising method to a part of the first-year HSC data to show that the observed mass distribution is statistically consistent with the prediction from the standard ΛCDM model." @default.
• W3151390452 created "2021-04-13" @default.
• W3151390452 creator A5020197083 @default.
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• W3151390452 date "2021-04-09" @default.
• W3151390452 modified "2023-10-15" @default.
• W3151390452 title "Noise reduction for weak lensing mass mapping: an application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data" @default.
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• W3151390452 doi "https://doi.org/10.1093/mnras/stab982" @default.
• W3151390452 hasPublicationYear "2021" @default.
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