Noise reduction for weak lensing mass mapping: An application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data. (arXiv:1911.12890v2 [astro-ph.CO] UPDATED)
<a href="http://arxiv.org/find/astro-ph/1/au:+Shirasaki_M/0/1/0/all/0/1">Masato Shirasaki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Moriwaki_K/0/1/0/all/0/1">Kana Moriwaki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Oogi_T/0/1/0/all/0/1">Taira Oogi</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Yoshida_N/0/1/0/all/0/1">Naoki Yoshida</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ikeda_S/0/1/0/all/0/1">Shiro Ikeda</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Nishimichi_T/0/1/0/all/0/1">Takahiro Nishimichi</a>
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 25000 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 $60%$ of the peaks in
the denoised maps with height greater than $5sigma$ 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 $Lambda$CDM model.
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 25000 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 $60%$ of the peaks in
the denoised maps with height greater than $5sigma$ 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 $Lambda$CDM model.
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