{
  "id": "1909.08060",
  "version": "v1",
  "published": "2019-09-17T19:49:11.000Z",
  "updated": "2019-09-17T19:49:11.000Z",
  "title": "Identification of Light Sources using Artificial Neural Networks",
  "authors": [
    "Chenglong You",
    "Narayan Bhusal",
    "Aidan Lambert",
    "Chao Dong",
    "Armando Perez-Leija",
    "Roberto de J. Leon-Montiel",
    "Amir Javaid",
    "Omar S. Magana-Loaiza"
  ],
  "comment": "5 pages, 5 figures",
  "categories": [
    "quant-ph",
    "physics.optics"
  ],
  "abstract": "The identification of light sources represents a task of utmost importance for the development of multiple photonic technologies. Over the last decades, the identification of light sources as diverse as sunlight, laser radiation and molecule fluorescence has relied on the collection of photon statistics. In general, this task requires an extensive number of measurements to unveil the characteristic statistical fluctuations and correlation properties of light, particularly in the low-photon flux regime. In this letter, we exploit the self-learning features of artificial neural networks and naive Bayes classifier to dramatically reduce the number of measurements required to discriminate thermal light from coherent light at the single-photon level. We demonstrate robust light identification with tens of measurements at mean photon numbers below one. Our protocols demonstrate an improvement in terms of the number of measurements of several orders of magnitude with respect to conventional schemes for characterization of light sources. Our work has important implications for multiple photonic technologies such as LIDAR and microscopy.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-09-17T19:49:11.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "artificial neural networks",
      "multiple photonic technologies",
      "demonstrate robust light identification",
      "measurements",
      "light sources represents"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}