Automatic anomaly detection in high energy collider data

Created by W.Langdon from gp-bibliography.bib Revision:1.7975

@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