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Galaxy clustering from the bottom up: A Streaming Model emulator I

Cuesta-Lazaro, Carolina; Nishimichi, Takahiro; Kobayashi, Yosuke; Ruan, Cheng-Zong; Eggemeier, Alexander; Miyatake, Hironao; Takada, Masahiro; Yoshida, Naoki; Zarrouk, Pauline; Baugh, Carlton M; Bose, Sownak; Li, Baojiu

Galaxy clustering from the bottom up: A Streaming Model emulator I Thumbnail


Carolina Cuesta-Lazaro

Takahiro Nishimichi

Yosuke Kobayashi

Cheng-Zong Ruan

Alexander Eggemeier

Hironao Miyatake

Masahiro Takada

Naoki Yoshida

Pauline Zarrouk


In this series of papers, we present a simulation-based model for the non-linear clustering of galaxies based on separate modelling of clustering in real space and velocity statistics. In the first paper, we present an emulator for the real-space correlation function of galaxies, whereas the emulator of the real-to-redshift space mapping based on velocity statistics is presented in the second paper. Here, we show that a neural network emulator for real-space galaxy clustering trained on data extracted from the DARK QUEST suite of N-body simulations achieves sub-per cent accuracies on scales 1 < r < 30 h−1 Mpc, and better than 3% on scales r < 1 h−1 Mpc in predicting the clustering of dark-matter haloes with number density 10−3.5 (h−1 Mpc)−3, close to that of SDSS LOWZ-like galaxies. The halo emulator can be combined with a galaxy-halo connection model to predict the galaxy correlation function through the halo model. We demonstrate that we accurately recover the cosmological and galaxy-halo connection parameters when galaxy clustering depends only on the mass of the galaxies’ host halos. Furthermore, the constraining power in σ8 increases by about a factor of 2 when including scales smaller than 5 h−1 Mpc. However, when mass is not the only property responsible for galaxy clustering, as observed in hydrodynamical or semi-analytic models of galaxy formation, our emulator gives biased constraints on σ8. This bias disappears when small scales (r < 10 h−1 Mpc) are excluded from the analysis. This shows that a vanilla halo model could introduce biases into the analysis of future datasets.


Cuesta-Lazaro, C., Nishimichi, T., Kobayashi, Y., Ruan, C., Eggemeier, A., Miyatake, H., …Li, B. (2023). Galaxy clustering from the bottom up: A Streaming Model emulator I. Monthly Notices of the Royal Astronomical Society,

Journal Article Type Article
Acceptance Date Apr 11, 2023
Online Publication Date Apr 25, 2023
Publication Date 2023
Deposit Date May 30, 2023
Publicly Available Date May 30, 2023
Journal Monthly Notices of the Royal Astronomical Society
Print ISSN 0035-8711
Electronic ISSN 1365-2966
Publisher Royal Astronomical Society
Peer Reviewed Peer Reviewed
Public URL


Accepted Journal Article (7 Mb)

Copyright Statement
This is a pre-copyedited, author-produced PDF of an article accepted for publication in Monthly Notices of the Royal Astronomical Society following peer review. The version of record is available online at:

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