21cmVAE: A Very Accurate Emulator of the 21 cm Global Signal

Abstract

Considerable observational efforts are being dedicated to measuring the sky-averaged (global) 21 cm signal of neutral hydrogen from Cosmic Dawn and the Epoch of Reionization. Deriving observational constraints on the astrophysics of this era requires modeling tools that can quickly and accurately generate theoretical signals across the wide astrophysical parameter space. For this purpose artificial neural networks were used to create the only two existing global signal emulators, 21 cm GEM and globalemu . In this paper we introduce 21 cm VAE, a neural-network-based global signal emulator, trained on the same data set of ∼30,000 global signals as the other two emulators, but with a more direct prediction algorithm that prioritizes accuracy and simplicity. Using neural networks, we compute derivatives of the signals with respect to the astrophysical parameters and establish the most important astrophysical processes that drive the global 21 cm signal at different epochs. 21 cm VAE has a relative rms error of only 0.34%—equivalently 0.54 mK—on average, which is a significant improvement compared to the existing emulators, and a run time of 0.04 s per parameter set. The emulator, the code, and the processed data sets are publicly available at https://github.com/christianhbye/21cmVAE and through https://zenodo.org/record/5904939 .