21SSD: a public database of simulated 21-cm signals from the epoch of reionization

TL;DR

This paper introduces 21SSD, a publicly accessible database of simulated 21-cm signals from the Epoch of Reionization, providing high-resolution templates for upcoming observational efforts and analysis method development.

Contribution

It presents a comprehensive set of simulated 21-cm brightness temperature lightcones and derived statistics, covering a wide parameter space for the first time in a publicly available database.

Findings

Power spectrum and PDF can distinguish between different models.

The database enables analysis of model parameter variations.

Simulations include X-ray and Lyman line transfer effects.

Abstract

The 21-cm signal from the Epoch of Reionization (EoR) is expected to be detected in the next few years, either with existing instruments or by the upcoming SKA and HERA projects. In this context there is a pressing need for publicly available high-quality templates covering a wide range of possible signals. These are needed both for end-to-end simulations of the up-coming instruments, as well as to develop signal analysis methods. In this work we present such a set of templates, publicly available, for download at https://21ssd.obspm.fr/. The database contains 21-cm brightness temperature lightcones at high and low resolution, and several derived statistical quantities for 45 models spanning our choice of 3D parameter space. These data are the result of fully coupled radiative hydrodynamic high resolution ($1024^3$) simulations performed with the LICORICE code. Both X-ray and Lyman line transfer is performed to account for heating and Wouthuysen-Field coupling fluctuations. We also present a first exploitation of the data using the power spectrum and the Pixel Distribution Function (PDF) as functions of redshifts, computed from lightcone data. We analyse how these two quantities behave when varying the model parameters while taking into account the thermal noise expected of a typical SKA survey. Finally, we show that the power spectrum and the PDF have different -- and to some extent complementary -- abilities to distinguish between different models. This opens the door to formulating an optimal sampling of the parameter space, dependant on the chosen diagnostics.