Denoising weak lensing mass maps with deep learning

Masato Shirasaki, Naoki Yoshida, and Shiro Ikeda
Phys. Rev. D 100, 043527 – Published 16 August 2019

Abstract

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 galaxy shape distortions and to the limited number density of the source galaxies. In this paper, we explore a deep-learning approach to reduce the noise. We develop an image-to-image translation method with conditional adversarial networks (CANs), which learn efficient mapping from an input noisy weak lensing map to the underlying noise field. We train the CANs using 30000 image pairs obtained from 1000 ray-tracing simulations of weak gravitational lensing. We show that the trained CANs reproduce the true one-point probability distribution function (PDF) of the noiseless lensing map with a bias less than 1σ on average, where σ is the statistical error. We perform a Fisher analysis to make a forecast for cosmological parameter inference with the one-point lensing PDF. By our denoising method using CANs, the first derivative of the PDF with respect to the cosmic mean matter density and the amplitude of the primordial curvature perturbations becomes larger by 50%. This allows us to improve the cosmological constraints by 30%40% using observational data from ongoing and upcoming galaxy imaging surveys.

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  • Received 30 January 2019

DOI:https://doi.org/10.1103/PhysRevD.100.043527

© 2019 American Physical Society

Physics Subject Headings (PhySH)

Gravitation, Cosmology & Astrophysics

Authors & Affiliations

Masato Shirasaki1,*, Naoki Yoshida2,3,4, and Shiro Ikeda5,6

  • 1National Astronomical Observatory of Japan (NAOJ), Mitaka, Tokyo 181-8588, Japan
  • 2Department of Physics, University of Tokyo, Tokyo 113-0033, Japan
  • 3Kavli Institute for the Physics and Mathematics of the Universe (WPI), University of Tokyo, Kashiwa, Chiba 277-8583, Japan
  • 4Institute for Physics of Intelligence, University of Tokyo, Tokyo 113-0033, Japan
  • 5The Institute of Statistical Mathematics, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan
  • 6Department of Statistical Science, Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, Japan

  • *masato.shirasaki@nao.ac.jp

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Vol. 100, Iss. 4 — 15 August 2019

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