---
_id: '17551'
abstract:
- lang: eng
  text: We present cosmological constraints from the Subaru Hyper Suprime-Cam (HSC)
    first-year weak lensing shear catalogue using convolutional neural networks (CNNs)
    and conventional summary statistics. We crop 19 3×3deg2 sub-fields from the first-year
    area, divide the galaxies with redshift 0.3≤z≤1.5 into four equally-spaced redshift
    bins, and perform tomographic analyses. We develop a pipeline to generate simulated
    convergence maps from cosmological N-body simulations, where we account for effects
    such as intrinsic alignments (IAs), baryons, photometric redshift errors, and
    point spread function errors, to match characteristics of the real catalogue.
    We train CNNs that can predict the underlying parameters from the simulated maps,
    and we use them to construct likelihood functions for Bayesian analyses. In the
    Λ cold dark matter model with two free cosmological parameters Ωm and σ8, we find
    Ωm=0.278+0.037−0.035, S8≡(Ωm/0.3)0.5σ8=0.793+0.017−0.018, and the IA amplitude
    AIA=0.20+0.55−0.58. In a model with four additional free baryonic parameters,
    we find Ωm=0.268+0.040−0.036, S8=0.819+0.034−0.024, and AIA=−0.16+0.59−0.58, with
    the baryonic parameters not being well-constrained. We also find that statistical
    uncertainties of the parameters by the CNNs are smaller than those from the power
    spectrum (5--24 percent smaller for S8 and a factor of 2.5--3.0 smaller for Ωm),
    showing the effectiveness of CNNs for uncovering additional cosmological information
    from the HSC data. With baryons, the S8 discrepancy between HSC first-year data
    and Planck 2018 is reduced from ∼2.2σ to 0.3--0.5σ.
article_processing_charge: No
article_type: original
author:
- first_name: Tianhuan
  full_name: Lu, Tianhuan
  last_name: Lu
- first_name: Zoltán
  full_name: Haiman, Zoltán
  id: 7c006e8c-cc0d-11ee-8322-cb904ef76f36
  last_name: Haiman
- first_name: Xiangchong
  full_name: Li, Xiangchong
  last_name: Li
citation:
  ama: Lu T, Haiman Z, Li X. Cosmological constraints from HSC survey first-year data
    using deep learning. <i>Monthly Notices of the Royal Astronomical Society</i>.
    2023;521(2):2050-2066. doi:<a href="https://doi.org/10.1093/mnras/stad686">10.1093/mnras/stad686</a>
  apa: Lu, T., Haiman, Z., &#38; Li, X. (2023). Cosmological constraints from HSC
    survey first-year data using deep learning. <i>Monthly Notices of the Royal Astronomical
    Society</i>. Oxford University Press. <a href="https://doi.org/10.1093/mnras/stad686">https://doi.org/10.1093/mnras/stad686</a>
  chicago: Lu, Tianhuan, Zoltán Haiman, and Xiangchong Li. “Cosmological Constraints
    from HSC Survey First-Year Data Using Deep Learning.” <i>Monthly Notices of the
    Royal Astronomical Society</i>. Oxford University Press, 2023. <a href="https://doi.org/10.1093/mnras/stad686">https://doi.org/10.1093/mnras/stad686</a>.
  ieee: T. Lu, Z. Haiman, and X. Li, “Cosmological constraints from HSC survey first-year
    data using deep learning,” <i>Monthly Notices of the Royal Astronomical Society</i>,
    vol. 521, no. 2. Oxford University Press, pp. 2050–2066, 2023.
  ista: Lu T, Haiman Z, Li X. 2023. Cosmological constraints from HSC survey first-year
    data using deep learning. Monthly Notices of the Royal Astronomical Society. 521(2),
    2050–2066.
  mla: Lu, Tianhuan, et al. “Cosmological Constraints from HSC Survey First-Year Data
    Using Deep Learning.” <i>Monthly Notices of the Royal Astronomical Society</i>,
    vol. 521, no. 2, Oxford University Press, 2023, pp. 2050–66, doi:<a href="https://doi.org/10.1093/mnras/stad686">10.1093/mnras/stad686</a>.
  short: T. Lu, Z. Haiman, X. Li, Monthly Notices of the Royal Astronomical Society
    521 (2023) 2050–2066.
date_created: 2024-09-05T10:14:34Z
date_published: 2023-03-06T00:00:00Z
date_updated: 2024-09-18T10:09:01Z
day: '06'
doi: 10.1093/mnras/stad686
extern: '1'
intvolume: '       521'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1093/mnras/stad686
month: '03'
oa: 1
oa_version: Published Version
page: 2050-2066
publication: Monthly Notices of the Royal Astronomical Society
publication_identifier:
  issn:
  - 0035-8711
  - 1365-2966
publication_status: published
publisher: Oxford University Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Cosmological constraints from HSC survey first-year data using deep learning
type: journal_article
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
volume: 521
year: '2023'
...