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Cited by 151Cosmology and Gravitation TheoriesGalaxies: Formation, Evolution, PhenomenaDark Matter and Cosmic Phenomena

Cosmological constraints with deep learning from KiDS-450 weak lensing maps

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Fluri, Janis, Kacprzak, Tomasz, Lucchi, Aurelien, Refregier, Alexandre, Amara, Adam, Hofmann, Thomas, Schneider, Aurel·2019·Physical Review D
DOI 10.1103/physrevd.100.063514
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Abstract

Convolutional neural networks (CNNs) have recently been demonstrated on synthetic data to improve upon the precision of cosmological inference. In particular, they have the potential to yield more precise cosmological constraints from weak lensing mass maps than the two-point functions. We present the cosmological results with a CNN from the KiDS-450 tomographic weak lensing dataset, constraining the total matter density ${\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}$, the fluctuation amplitude ${\ensuremath{\sigma}}_{8}$, and the intrinsic alignment amplitude ${A}_{\mathrm{IA}}$. We use a grid of N-body simulations to generate a training set of tomographic weak lensing maps. We test the robustness of the expected constraints to various effects, such as baryonic feedback, simulation accuracy, a different value of ${H}_{0}$, or the light cone projection technique. We train a set of ResNet-based CNNs with varying depths to analyze sets of tomographic KiDS mass maps divided into 20 flat regions, with applied Gaussian smoothing of $\ensuremath{\sigma}=2.34\text{ }\text{ }\mathrm{arc}\text{ }\mathrm{min}$. The uncertainties on shear calibration and $n(z)$ error are marginalized in the likelihood pipeline. Following a blinding scheme, we derive constraints on ${S}_{8}={\ensuremath{\sigma}}_{8}({\mathrm{\ensuremath{\Omega}}}_{\mathrm{m}}/0.3{)}^{0.5}=0.77{7}_{\ensuremath{-}0.036}^{+0.038}$ with our CNN analysis, with ${A}_{\mathrm{IA}}=1.39{8}_{\ensuremath{-}0.724}^{+0.779}$. We compare this result to the power spectrum analysis on the same maps and likelihood pipeline and find an improvement of about 30% for the CNN. We discuss how our results offer excellent prospects for the use of deep learning in future cosmological data analysis.

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  • Fluri et al. (2019) found constraints broadly consistent with the fiducial 2pt function analysis of KiDS-450, but when compared with an internal power-spectrum analysis yielded a 30% tighter posterior.

    “This study found constraints broadly consistent with the fiducial 2pt function analysis of KiDS-450 (Hildebrandt et al. 2017) but when compared with an internal power-spectrum analysis yielded a 30% tighter posterior.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Fluri et al. (2019)

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Sorted by citations

Authors
Fluri, J., Kacprzak, T., Lucchi, A., Refregier, A., Amara, A., Hofmann, T., & Schneider, A.
Affiliations
ETH Zurich (CH)
ORCID
Venue
Physical Review D
Publisher
American Physical Society (APS)
Published
2019-09-13
Volume
100
Issue
6
ISSN
  • 2470-0010 (ISSN-L)
  • 2470-0029
DOI
10.1103/physrevd.100.063514
OpenAlex
W2948489233
Cited by
151
References
81
Citations by year
2026: 10 · 2025: 19 · 2024: 21 · 2023: 27 · 2022: 25 · 2021: 25 · 2020: 21 · 2019: 3
FWCI
9.88
Type
journal article
Language
en
Open access
Licence
link.aps.org/licenses/aps-default-license (vor)
Locations
  • Physical review. D/Physical review. D. (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
  • American Astronomical Society Meeting Abstracts #235
Funders
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (200021_169130, PZ00P2_161363)
  • Swiss Data Science Centre (sd01)
Topics
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Cosmology and Gravitation Theories
  • Physical Sciences › Physics and Astronomy › Astronomy and Astrophysics › Galaxies: Formation, Evolution, Phenomena
  • Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › Dark Matter and Cosmic Phenomena
Keywords
cosmological constraints, convolutional neural networks, KiDS-450, tomographic weak lensing, mass maps, S8, matter density, intrinsic alignment, ResNet, N-body simulations, shear calibration, power spectrum comparison, baryonic feedback

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