The LoReLi database: 21 cm signal inference with 3D radiative hydrodynamics simulations

Abstract

The Square Kilometer Array is expected to measure the 21 cm signal from the Epoch of Reionization (EoR) in the coming decade, and its pathfinders may provide a statistical detection even earlier. The currently reported upper limits provide tentative constraints on the astrophysical parameters of the models of the EoR. In order to interpret such data with 3D radiative hydrodynamics simulations using Bayesian inference, we present the latest developments of the Licorice code. Relying on an implementation of the halo conditional mass function to account for unresolved star formation, this code now allows accurate simulations of the EoR at 2563 resolution. We use this version of Licorice to produce the first iteration of LoReLi, a public dataset now containing hundreds of 21 cm signals computed from radiative hydrodynamics simulations. We train a neural network on LoReLi to provide a fast emulator of the Licorice power spectra, LorEMU, which has ∼5% rms error relative to the simulated signals. LorEMU is used in a Markov chain Monte Carlo framework to perform Bayesian inference, first on a mock observation composed of a simulated signal and thermal noise corresponding to 100 h observations with the SKA. We then apply our inference pipeline to the latest measurements from the HERA interferometer. We report constraints on the X-ray emissivity, and confirm that cold reionization scenarios are unlikely to accurately represent our Universe.