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Emulating the Global 21-cm Signal from Cosmic Dawn and Reionization

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arxiv 1910.06274 v2 pith:6GKXVPA6 submitted 2019-10-14 astro-ph.CO

classification astro-ph.CO
keywords signalastrophysicalcosmicdawnfirstglobalparametersdatabase
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The 21-cm signal of neutral hydrogen is a sensitive probe of the Epoch of Reionization (EoR) and Cosmic Dawn. Currently operating radio telescopes have ushered in a data-driven era of 21-cm cosmology, providing the first constraints on the astrophysical properties of sources that drive this signal. However, extracting astrophysical information from the data is highly non-trivial and requires the rapid generation of theoretical templates over a wide range of astrophysical parameters. To this end emulators are often employed, with previous efforts focused on predicting the power spectrum. In this work we introduce 21cmGEM - the first emulator of the global 21-cm signal from Cosmic Dawn and the EoR. The smoothness of the output signal is guaranteed by design. We train neural networks to predict the cosmological signal using a database of ~30,000 simulated signals which were created by varying seven astrophysical parameters: the star formation efficiency and the minimal mass of star-forming halos; the efficiency of the first X-ray sources and their spectrum parameterized by spectral index and the low energy cutoff; the mean free path of ionizing photons and the CMB optical depth. We test the performance with a set of ~2,000 simulated signals, showing that the relative error in the prediction has an r.m.s. of 0.0159. The algorithm is efficient, with a running time per parameter set of 0.16 sec. Finally, we use the database of models to check the robustness of relations between the features of the global signal and the astrophysical parameters that we previously reported.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Likelihood-free Model Selection in Cosmic Reionization with Three-dimensional Tomographic 21 cm Lightcone Images

    astro-ph.IM 2025-02 conditional novelty 6.0 of 10

    The authors apply likelihood-free model selection with a CNN summary and nested sampling to mock 21 cm reionization lightcones; the correct toy model is always favored, but posteriors for outside-in models are biased.

  2. Machine Learning and the SKA for Cosmic Dawn and the Epoch of Reionization

    astro-ph.IM 2026-07 accept novelty 2.5 of 10

    A multi-author overview of machine-learning algorithms proposed for instrument modelling, data analysis, simulation and inference in SKA Cosmic Dawn and Epoch of Reionization science.

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