Multivariate Techniques for Identifying Diffractive Interactions at the LHC

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

@Misc{oai:arXiv.org:0909.3039,
  title =        "Multivariate Techniques for Identifying Diffractive
                 Interactions at the LHC",
  note =         "Comment: 31 pages, 14 figures, 11 tables",
  author =       "Mikael Kuusela and Jerry W. Lamsa and Eric Malmi and 
                 Petteri Mehtala and Risto Orava",
  year =         "2009",
  month =        sep # "~16",
  howpublished = "arXiv",
  keywords =     "genetic algorithms, genetic programming, Gene
                 Expression Programming, High Energy Physics -
                 Experiment, High Energy Physics - Phenomenology",
  size =         "32 pages",
  abstract =     "Close to one half of the LHC events are expected to be
                 due to elastic or inelastic diffractive scattering.
                 Still, predictions based on extrapolations of
                 experimental data at lower energies differ by large
                 factors in estimating the relative rate of diffractive
                 event categories at the LHC energies. By identifying
                 diffractive events, detailed studies on proton
                 structure can be carried out. The combined forward
                 physics objects: rapidity gaps, forward multiplicity
                 and transverse energy flows can be used to efficiently
                 classify proton-proton collisions. Data samples
                 recorded by the forward detectors, with a simple
                 extension, will allow first estimates of the single
                 diffractive (SD), double diffractive (DD), central
                 diffractive (CD), and non-diffractive (ND) cross
                 sections. The approach, which uses the measurement of
                 inelastic activity in forward and central detector
                 systems, is complementary to the detection and
                 measurement of leading beam-like protons. In this
                 investigation, three different multivariate analysis
                 approaches are assessed in classifying forward physics
                 processes at the LHC. It is shown that with gene
                 expression programming, neural networks and support
                 vector machines, diffraction can be efficiently
                 identified within a large sample of simulated
                 proton-proton scattering events. The event
                 characteristics are visualized by using the
                 self-organizing map algorithm.",
  bibsource =    "OAI-PMH server at export.arxiv.org",
  oai =          "oai:arXiv.org:0909.3039",
  URL =          "http://arxiv.org/abs/0909.3039",
}

Genetic Programming entries for Mikael Kuusela Jerry W Lamsa Eric Malmi Petteri Mehtala Risto Orava

Citations