Issue |
A&A
Volume 684, April 2024
|
|
---|---|---|
Article Number | A57 | |
Number of page(s) | 17 | |
Section | Cosmology (including clusters of galaxies) | |
DOI | https://doi.org/10.1051/0004-6361/202348640 | |
Published online | 04 April 2024 |
Galaxy clustering multi-scale emulation
Aix-Marseille Univ., CNRS/IN2P3, CPPM, Marseille, France
e-mail: dumerchat@cppm.in2p3.fr
Received:
16
November 2023
Accepted:
23
December 2023
Simulation-based inference has seen increasing interest in the past few years as a promising approach to modelling the non-linear scales of galaxy clustering. The common approach, using the Gaussian process, is to train an emulator over the cosmological and galaxy–halo connection parameters independently for every scale. We present a new Gaussian process model that allows the user to extend the input parameter space dimensions and to use a non-diagonal noise covariance matrix. We use our new framework to simultaneously emulate every scale of the non-linear clustering of galaxies in redshift space from the ABACUSSUMMITN-body simulations at redshift z = 0.2. The model includes nine cosmological parameters, five halo occupation distribution (HOD) parameters, and one scale dimension. Accounting for the limited resolution of the simulations, we train our emulator on scales from 0.3 h−1 Mpc to 60 h−1 Mpc and compare its performance with the standard approach of building one independent emulator for each scale. The new model yields more accurate and precise constraints on cosmological parameters compared to the standard approach. As our new model is able to interpolate over the scale space, we are also able to account for the Alcock-Paczynski distortion effect, leading to more accurate constraints on the cosmological parameters.
Key words: galaxies: general / large-scale structure of Universe
© The Authors 2024
Open Access article, published by EDP Sciences, under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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