{
  "id": "1712.09114",
  "version": "v1",
  "published": "2017-12-25T18:57:00.000Z",
  "updated": "2017-12-25T18:57:00.000Z",
  "title": "HEPDrone: a toolkit for the mass application of machine learning in High Energy Physics",
  "authors": [
    "Sean Benson",
    "Konstantin Gizdov"
  ],
  "comment": "5 pages, 5 figures",
  "categories": [
    "hep-ex",
    "physics.data-an"
  ],
  "abstract": "Machine learning has proven to be an indispensable tool in the selection of interesting events in high energy physics. Such technologies will become increasingly important as detector upgrades are introduced and data rates increase by orders of magnitude. We propose a toolkit to enable the creation of a drone classifier from any machine learning classifier, such that different classifiers may be standardised into a single form and executed in parallel. We demonstrate the capability of the drone neural network to learn the required properties of the input neural network without the use of any training data, only using appropriate questioning of the input neural network.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-12-25T18:57:00.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "high energy physics",
      "machine learning",
      "mass application",
      "input neural network",
      "data rates increase"
    ],
    "tags": [
      "research tool"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 5,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}