{
  "id": "1708.06706",
  "version": "v1",
  "published": "2017-08-22T16:27:13.000Z",
  "updated": "2017-08-22T16:27:13.000Z",
  "title": "Analyzing γ-rays of the Galactic Center with Deep Learning",
  "authors": [
    "Sascha Caron",
    "Germán A. Gómez-Vargas",
    "Luc Hendriks",
    "Roberto Ruiz de Austri"
  ],
  "comment": "24 pages, 11 figures",
  "categories": [
    "astro-ph.HE",
    "hep-ph"
  ],
  "abstract": "We present a new method to interpret the $\\gamma$-ray data of our inner Galaxy as measured by the Fermi Large Area Telescope (Fermi LAT). We train and test convolutional neural networks with simulated Fermi-LAT images based on models tuned to real data. We use this method to investigate the origin of an excess emission of GeV $\\gamma$-rays seen in previous studies. Interpretations of this excess include $\\gamma$ rays created by the annihilation of dark matter particles and $\\gamma$ rays originating from a collection of unresolved point sources, such as millisecond pulsars. Our new method allows precise measurements of the contribution and properties of an unresolved population of $\\gamma$-ray point sources in the interstellar diffuse emission model.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-08-22T16:27:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "galactic center",
      "deep learning",
      "fermi large area telescope",
      "interstellar diffuse emission model",
      "test convolutional neural networks"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 24,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}