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arXiv · 2310.02684

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

Romain Meriot·Benoit Semelin
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Abstract

The Square Kilometer array is expected to measure the 21cm 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 \textsc{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 $256^3$ resolution. We use this version of \textsc{Licorice} to produce the first iteration of \textsc{LoReLi}, a public dataset now containing hundreds of 21cm signals computed from radiative hydrodynamics simulations. We train a neural network on \textsc{LoReLi} to provide a fast emulator of the \textsc{Licorice} power spectra, \textsc{LorEMU}, which has $\sim 5\%$ rms error relative to the simulated signals. \textsc{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 100h 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.

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Romain Meriot, Benoit Semelin. 2023-12-01. The LoReLi database: 21 cm signal inference with 3D radiative hydrodynamics simulations. https://arxiv.org/abs/2310.02684

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