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Cited by 83Cosmology and Gravitation TheoriesGalaxies: Formation, Evolution, PhenomenaDark Matter and Cosmic Phenomena

Cosmological constraints from noisy convergence maps through deep learning

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Fluri, Janis, Kacprzak, Tomasz, Refregier, Alexandre, Amara, Adam, Lucchi, Aurelien, Hofmann, Thomas·2018·Physical Review D
DOI 10.1103/physrevd.98.123518
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Abstract

Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Because of its ability to learn relevant features from the data, it is able to extract more information from the mass maps than the commonly used power spectrum, and thus achieve better precision for cosmological parameter measurement. We explore the advantage of convolutional neural networks over the power spectrum for varying levels of shape noise and different smoothing scales applied to the maps. We compare the cosmological constraints from the two methods in the ${\mathrm{\ensuremath{\Omega}}}_{M}\ensuremath{-}{\ensuremath{\sigma}}_{8}$ plane for sets of $400\text{ }\text{ }{\mathrm{deg}}^{2}$ convergence maps. We find that, for a shape noise level corresponding to $8.53\text{ }\text{ }\mathrm{galaxies}/{\mathrm{arcmin}}^{2}$ and the smoothing scale of ${\ensuremath{\sigma}}_{s}=2.34\text{ }\text{ }\mathrm{arcmin}$, the network is able to generate 45% tighter constraints. For a smaller smoothing scale of ${\ensuremath{\sigma}}_{s}=1.17$ the improvement can reach $\ensuremath{\sim}50%$, while for a larger smoothing scale of ${\ensuremath{\sigma}}_{s}=5.85$, the improvement decreases to 19%. The advantage generally decreases when the noise level and smoothing scales increase. We present a new training strategy to train the neural network with noisy data, as well as considerations for practical applications of the deep learning approach.

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Crossref · 10.1103/physrevd.98.123518

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58 of 58 references verified0 citation errors · 0 not checkable

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  • The output of a CNN trained for regression is not an unbiased estimator for cosmological parameters, but should be interpreted as a low-dimensional summary statistic for inference in a second step.

    “It is important to note, as reported in all these papers, that the output of the network trained for regression will not be an unbiased estimator for the cosmological parameters, but should be interpreted as a low-dimensional summary statistic, which can then be used for inference in a second step, independent from the network training.”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Fluri et al. 2018

  • All these papers reported the ability to extract more information than a 2pt function analysis, even on realistically noisy data.

    “With this approach, all these papers reported the ability to extract more information than a 2pt function analysis, even on realistically noisy data”

    From The Dawes Review 10: The impact of deep learning for the analysis of galaxy surveys (2023) · cites this paper as Fluri et al. 2018

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Authors
Fluri, J., Kacprzak, T., Refregier, A., Amara, A., Lucchi, A., & Hofmann, T.
Affiliations
ETH Zurich (CH)
ORCID
Venue
Physical Review D
Publisher
American Physical Society (APS)
Published
2018-12-18
Volume
98
Issue
12
ISSN
  • 2470-0010 (ISSN-L)
  • 2470-0029
DOI
10.1103/physrevd.98.123518
OpenAlex
W2884612602
Cited by
83
References
65
Citations by year
2026: 1 · 2025: 15 · 2024: 15 · 2023: 9 · 2022: 13 · 2021: 9 · 2020: 10 · 2019: 11
FWCI
4.02
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)
Funders
  • Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
  • Swiss Data Science Centre (SDSC) (200021_169130)
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 parameter constraints, weak gravitational lensing, convergence maps, convolutional neural networks, shape noise, smoothing scale, matter density parameter, sigma_8, power spectrum comparison

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