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

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

Abstract

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.

Citation

Cuesta-Lazaro, C., Nishimichi, T., Kobayashi, Y., Ruan, C.-Z., Eggemeier, A., Miyatake, H., Takada, M., Yoshida, N., Zarrouk, P., Baugh, C. M., Bose, S., & Li, B. (2023). Galaxy clustering from the bottom up: A Streaming Model emulator I. Monthly Notices of the Royal Astronomical Society, 523(3), 3219–3238. https://doi.org/10.1093/mnras/stad1207

• Journal Article Type: Article
• Acceptance Date: Apr 11, 2023
• Online Publication Date: Apr 25, 2023
• Publication Date: 2023-08
• 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
• Volume: 523
• Issue: 3
• Pages: 3219–3238
• DOI: https://doi.org/10.1093/mnras/stad1207
• Public URL: https://durham-repository.worktribe.com/output/1173536