Dark Quest II: A Wide-Coverage Neural Network Emulator of the Nonlinear Matter Power Spectrum Across Extended Cosmologies

arXiv:2605.28596v1 Announce Type: cross Abstract: extsc{DarkEmulator2} is a neural network emulator of the nonlinear matter power spectrum in a nine-dimensional w_0 w_a nu o CDM parameter space, developed as the emulator component of the extsc{Dark Quest II} (DQ2) program. It is trained on simulations generated with the extsc{Ginkaku} code, whose numerical implementation, accuracy tests, and post-processing pipeline are described in the companion paper. The design follows a unified strategy: in addition to the cosmological parameter vector, we supplement the neural network's inputs with three families of physically motivated auxiliary quantities -- the linear matter power spectrum, descriptors of the simulation resolution, and a low-dimensional summary of the initial Gaussian random field -- that are expected to improve generalization across the parameter space. Training a single network jointly across three simulation resolution tiers allows the emulator to exploit a small number of high-resolution simulations while retaining broad coverage from lower-resolution simulations. For a L_{box}=1,hiGpc box with N=3000^{3} particles, the emulator reproduces the simulated matter power spectrum to subpercent accuracy up to the particle Nyquist scale, k_{Ny}simeq 10,hMpci. The emulator remains accurate over the calibrated wavenumber range, while its highest-k predictions depend on the simulation resolution and shot noise. We validate the emulator on independent test suites and, through a cross-comparison with several public emulators and widely used fitting formulas, characterize the inter-model consistency and the parameter-dependent trends in their residuals.

Source: arXiv cs.LG | 2026-05-28