Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysis

Trusov, Svyatoslav; Zarrouk, Pauline; Cole, Shaun

Abstract

We present a neural network-based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3σ accuracy better than 0.5 per cent up to kmax = 0.25 hMpc−1 within Lambda-cold dark matter (˄CDM) and around 0.5 per cent w0–waCDM. Our surrogate model only emulates the galaxy bias-invariant terms of one-loop perturbation theory predictions, these terms are then combined analytically with galaxy bias terms, counter-terms, and stochastic terms in order to obtain the non-linear redshift-space galaxy power spectrum. This allows us to avoid any galaxy bias prescription in the training of the emulator, which makes it more flexible. Moreover, we include the redshift z ∈ [0, 1.4] in the training which further avoids the need for re-training the emulator. We showcase the performance of the emulator in recovering the cosmological parameters of ˄CDM by analysing the suite of 25 AbacusSummit simulations that mimic the Dark Energy Spectroscopic Instrument luminous red galaxies at z=0.5 and 0.8, together as the emission line galaxies at z=0.8. We obtain similar performance in all cases, demonstrating the reliability of the emulator for any galaxy sample at any redshift in 0 < z < 1.4. We will make our emulator public at github repository.

Citation

Trusov, S., Zarrouk, P., & Cole, S. (2025). Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysis. Monthly Notices of the Royal Astronomical Society, 538(3), 1789-1799. https://doi.org/10.1093/mnras/staf285

• Journal Article Type: Article
• Acceptance Date: Jan 13, 2025
• Online Publication Date: Feb 17, 2025
• Publication Date: 2025-04
• Deposit Date: May 27, 2025
• Publicly Available Date: May 27, 2025
• 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: 538
• Issue: 3
• Pages: 1789-1799
• DOI: https://doi.org/10.1093/mnras/staf285
• Public URL: https://durham-repository.worktribe.com/output/3964732