Nonlinearity-free prediction of the growth-rate <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>f</mml:mi><mml:msub><mml:mi>σ</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math> using convolutional neural networks-Reference-Cited by-同舟云学术

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Nonlinearity-free prediction of the growth-rate fσ8 using convolutional neural networks

Published:2024-07-19 Issue:2 Volume:110 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Murakami Koya¹^ORCID,Ocampo Indira²^ORCID,Nesseris Savvas²^ORCID,Nishizawa Atsushi J.³¹¹^ORCID,Kuroyanagi Sachiko²¹^ORCID

Affiliation:

1. Nagoya University

2. Instituto de Física Teórica UAM-CSIC

3. Gifu Shotoku Gakuen University

Funder

Consejo Superior de Investigaciones Científicas

Agencia Estatal de Investigación

Ministerio de Ciencia e Innovación

Japan Society for the Promotion of Science

Comunidad de Madrid

“la Caixa” Foundation

Nagoya University

Japan Science and Technology Agency

IFT Centro de Excelencia Severo Ochoa

Atracción de Talento

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.110.023525/fulltext

Reference58 articles.

1. Cosmological constant—the weight of the vacuum

2. Internal robustness: systematic search for systematic bias in SN Ia data

3. Planck 2018 results

4. Planck 2018 results

5. Dark Energy Survey Year 3 results: Cosmological constraints from galaxy clustering and weak lensing

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