Neural Network-based model of galaxy power spectrum: Fast full-shape galaxy power spectrum analysis

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\% up to $k_{\mathrm{max}}$=0.25$h^{-1}Mpc$ within $\Lambda$CDM and around 0.5\% $w_0$-$w_a$CDM. Our surrogate model only emulates the galaxy bias-invariant terms of 1-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 DESI Luminous Red Galaxies at $z=0.5$ and $z=0.8$, 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 $0 < z < 1.4$