"Subaru-HSC"

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

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 …

Denoising Weak Lensing Mass Maps with Deep Learning

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 actual observations suffer from noise due to imperfect measurement of …

The Hyper Suprime-Cam SSP Transient Survey in COSMOS: Overview

We present an overview of a deep transient survey of the COSMOS field with the Subaru Hyper Suprime-Cam (HSC). The survey was performed for the 1.77 deg ultra-deep layer and 5.78 deg deep layer in the Subaru Strategic Program over 6- and 4-month …

Machine-Learning selection of optical transients in Subaru/Hyper Suprime-Cam survey

We present an application of machine-learning (ML) techniques to source selection in the optical transient survey data with the Hyper Suprime-Cam (HSC) on the Subaru telescope. Our goal is to select real transient events accurately and in a timely …

Supernova candidates discovered with Subaru/Hyper Suprime-Cam

We report the discovery of 10 supernova candidates from a transient survey with Subaru/Hyper Suprime-Cam (HSC). Our Subaru/HSC open-use observations were performed on 19 Aug 2015 UT, under poor weather condition with 1.1-1.5 arcsec seeing. The …