Field-level simulation-based inference of galaxy clustering with convolutional neural networks-Reference-Cited by-同舟云学术

Field-level simulation-based inference of galaxy clustering with convolutional neural networks

Published:2024-04-30 Issue:8 Volume:109 Page:
ISSN:2470-0010
Container-title:Physical Review D
language:en
Short-container-title:Phys. Rev. D

Author:

Lemos Pablo^ORCID,Parker Liam,Hahn ChangHoon,Ho Shirley,Eickenberg Michael,Hou Jiamin,Massara Elena,Modi Chirag,Dizgah Azadeh Moradinezhad,Blancard Bruno Régaldo-Saint,Spergel David,

Funder

Simons Foundation

European Commission

H2020 Marie Sk?odowska-Curie Actions

Tomalla Foundation

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevD.109.083536/fulltext

Reference93 articles.

1. Clustering in real space and in redshift space

2. Towards optimal measurement of power spectra - I. Minimum variance pair weighting and the Fisher matrix

3. A measurement of the cosmological mass density from clustering in the 2dF Galaxy Redshift Survey

4. The 2dF Galaxy Redshift Survey: correlation functions, peculiar velocities and the matter density of the Universe

5. Cosmological constraints from the SDSS luminous red galaxies

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