Automatic anomaly detection in high energy collider data

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Misc{deVisscher:2011:arXiv,
  title =        "Automatic anomaly detection in high energy collider
                 data",
  author =       "Simon {de Visscher} and Michel Herquet",
  year =         "2011",
  month =        apr # "~13",
  keywords =     "genetic algorithms, genetic programming, high energy
                 physics, phenomenology, experiment, data analysis",
  abstract =     "We address the problem of automatic anomaly detection
                 in high energy collider data. Our approach is based on
                 the random generation of analytic expressions for
                 kinematical variables, which can then be evolved
                 following a genetic programming procedure to enhance
                 their discriminating power. We apply this approach to
                 three concrete scenarios to demonstrate its possible
                 usefulness, both as a detailed check of reference
                 Monte-Carlo simulations and as a model independent tool
                 for the detection of New Physics signatures.",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:1104.2404",
  URL =          "http://arxiv.org/abs/1104.2404",
  notes =        "Comment: 5 pages, 2 figures",
}

Genetic Programming entries for Simon de Visscher Michel Herquet

Citations