Neural network-based model of galaxy power spectrum: fast full-shape galaxy power spectrum analysis
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 3$$\sigma$$ accuracy better than 0.5 per cent up to $$k_{\mathrm{max}}$$ = 0.25 $$\, h\text{Mpc}^{-1}$$ within Lambda-cold dark matter ($$\Lambda$$CDM) and around 0.5 per cent $$w_0$$–$$w_a$$CDM. 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 $$z \in [0,1.4]$$ 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 $$\Lambda$$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 $$0 \lt z \lt 1.4$$. We will make our emulator public at github repository.
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- AC02-05CH11231; AC02-76SF00515; AC05-00OR22725; SC0012567; SC0013718
- OSTI ID:
- 2536788
- Journal Information:
- Monthly Notices of the Royal Astronomical Society, Journal Name: Monthly Notices of the Royal Astronomical Society Journal Issue: 3 Vol. 538; ISSN 0035-8711
- Publisher:
- Oxford University PressCopyright Statement
- Country of Publication:
- United Kingdom
- Language:
- English
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