{
  "id": "1910.06274",
  "version": "v1",
  "published": "2019-10-14T16:58:20.000Z",
  "updated": "2019-10-14T16:58:20.000Z",
  "title": "Emulating the Global 21-cm Signal from Cosmic Dawn and Reionization",
  "authors": [
    "Aviad Cohen",
    "Anastasia Fialkov",
    "Rennan Barkana",
    "Raul Monsalve"
  ],
  "comment": "16 pages, 15 figures, submitted to MNRAS",
  "categories": [
    "astro-ph.CO"
  ],
  "abstract": "The 21-cm signal of neutral hydrogen is a sensitive probe of the Epoch of Reionization, Cosmic Dawn and the Dark Ages. 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 Epoch of Reionization. The smoothness of the output signal is guaranteed by design. We train neural networks to predict the cosmological signal based on a seven-parameter astrophysical model, using a database of $\\sim$30,000 simulated signals. We test the performance with a set of $\\sim$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. In particular, we confirm the prediction that the coordinates of the maxima of the global signal, if measured, can be used to estimate the Ly{\\alpha} intensity and the X-ray intensity at early cosmic times.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-10-14T16:58:20.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "cosmic dawn",
      "reionization",
      "global signal",
      "simulated signals",
      "train neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 16,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}