Noise reduction for weak lensing mass mapping: An application of generative adversarial networks to Subaru Hyper Suprime-Cam first-year data

no code implementations • 28 Nov 2019 • Masato Shirasaki, Kana Moriwaki, Taira Oogi, Naoki Yoshida, Shiro Ikeda, Takahiro Nishimichi

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.

Denoising Ensemble Learning +1

Denoising Weak Lensing Mass Maps with Deep Learning

no code implementations • 14 Dec 2018 • Masato Shirasaki, Naoki Yoshida, Shiro Ikeda

Weak gravitational lensing is a powerful probe of the large-scale cosmic matter distribution.

Clustering Denoising +1

Exhaustive search for sparse variable selection in linear regression

no code implementations • 7 Jul 2017 • Yasuhiko Igarashi, Hikaru Takenaka, Yoshinori Nakanishi-Ohno, Makoto Uemura, Shiro Ikeda, Masato Okada

By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states.

Astronomy regression +1

Machine-learning Selection of Optical Transients in Subaru/Hyper Suprime-Cam Survey

no code implementations • 12 Sep 2016 • Mikio Morii, Shiro Ikeda, Nozomu Tominaga, Masaomi Tanaka, Tomoki Morokuma, katsuhiko Ishiguro, Junji Yamato, Naonori Ueda, Naotaka Suzuki, Naoki Yasuda, Naoki Yoshida

We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with Hyper Suprime-Cam (HSC) on the Subaru telescope.

Instrumentation and Methods for Astrophysics