Ten-dimensional neural network emulator for the nonlinear matter power spectrum

TL;DR

GokuNEmu is a fast, accurate ten-dimensional neural network emulator for the nonlinear matter power spectrum, supporting extended cosmological models and enabling efficient analysis of upcoming large-scale structure surveys.

Contribution

It introduces GokuNEmu, a novel 10D emulator that models extended cosmological parameters with high accuracy and speed, surpassing existing tools in scope and efficiency.

Findings

Achieves ~0.5% accuracy across a broad redshift and scale range.

Supports extended cosmological models beyond DM, including dark energy and neutrino parameters.

Predicts a cosmology in ~2 milliseconds, enabling rapid data analysis.

Abstract

We present GokuNEmu, a ten-dimensional neural network emulator for the nonlinear matter power spectrum, designed to support next-generation cosmological analyses. Built on the Goku $N$-body simulation suite and the T2N-MusE emulation framework, GokuNEmu predicts the matter power spectrum with $\sim 0.5 \%$ average accuracy for redshifts $0 \leq z \leq 3$ and scales $0.006 \leq k/(h\,\mathrm{Mpc}^{-1}) \leq 10$. The emulator models a 10D parameter space that extends beyond $\Lambda$CDM to include dynamical dark energy (characterized by $w_0$ and $w_a$), massive neutrinos ($\sum m_\nu$), the effective number of neutrinos ($N_\text{eff}$), and running of the spectral index ($\alpha_\text{s}$). Its broad parameter coverage, particularly for the extensions, makes it the only matter power spectrum emulator capable of testing recent dynamical dark energy constraints from DESI. In addition, it requires only $\sim $2 milliseconds to predict a single cosmology on a laptop, orders of magnitude faster than existing emulators. These features make GokuNEmu a uniquely powerful tool for interpreting observational data from upcoming surveys such as LSST, Euclid, the Roman Space Telescope, and CSST.