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Neural style transfer of weak lensing mass maps
Masato Shirasaki, Shiro Ikeda
We propose a new generative model of projected cosmic mass density maps inferred from weak gravitational lensing observations of distant galaxies (weak lensing mass maps). We const…
Three-Dimensional Reconstruction of Weak Lensing Mass Maps with a Sparsity Prior. I. Cluster Detection
Xiangchong Li, Naoki Yoshida, Masamune Oguri +2
We propose a novel method to reconstruct high-resolution three-dimensional mass maps using data from photometric weak-lensing surveys. We apply an adaptive LASSO algorithm to perfo…
Noise reduction for weak lensing mass mapping: An application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data
Masato Shirasaki, Kana Moriwaki, Taira Oogi +3
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 tr…
Denoising Weak Lensing Mass Maps with Deep Learning
Masato Shirasaki, Naoki Yoshida, Shiro Ikeda
Weak gravitational lensing is a powerful probe of the large-scale cosmic matter distribution. Wide-field galaxy surveys allow us to generate the so-called weak lensing maps, but ac…