Design and optimization of neural networks for multifidelity cosmological emulation

TL;DR

This paper introduces T2N-MusE, a neural network framework with innovative multifidelity architecture and optimization techniques, significantly improving the accuracy and efficiency of cosmological emulators for the matter power spectrum.

Contribution

The paper presents a novel neural network framework with a 2-step multifidelity architecture and advanced training strategies, outperforming traditional Gaussian process models in cosmological emulation.

Findings

Reduced mean error by over five times

Lowered worst-case error by about eight times

Built the most powerful matter power spectrum emulator, GokuNEmu

Abstract

Accurate and efficient simulation-based emulators are essential for interpreting cosmological survey data down to nonlinear scales. Multifidelity emulation techniques reduce simulation costs by combining high- and low-fidelity data, but traditional regression methods such as Gaussian processes struggle with scalability in sample size and dimensionality. In this work, we present T2N-MusE, a neural network framework characterized by (i) a novel 2-step multifidelity architecture, (ii) a 2-stage Bayesian hyperparameter optimization, (iii) a 2-phase $k$-fold training strategy, and (iv) a per-$z$ principal component analysis strategy. We apply T2N-MusE to selected data from the Goku simulation suite, covering a 10-dimensional cosmological parameter space, and build emulators for the matter power spectrum over a range of redshifts with different configurations. We find the emulators outperform our earlier Gaussian process models significantly and demonstrate that each of these techniques is efficient in training neural networks or/and effective in improving generalization accuracy. We observe a reduction in the mean error by more than a factor of five and in the worst-case error by approximately a factor of eight in leave-one-out cross-validation, relative to previous work. This framework has been used to build the most powerful emulator for the matter power spectrum, GokuNEmu, and will also be used to construct emulators for other statistics in future.