Multivariate Techniques for Identifying Diffractive Interactions at the LHC

Show simple item record

dc.contributor University of Helsinki, Helsinki Institute of Physics en
dc.contributor University of Helsinki, Department of Physics en Kuusela, Mikael Lämsä, Jerry W. Malmi, Eric Mehtälä, Petteri Orava, Risto 2010-12-16T14:37:37Z 2010-12-16T14:37:37Z 2010
dc.identifier.citation Kuusela , M , Lämsä , J W , Malmi , E , Mehtälä , P & Orava , R 2010 , ' Multivariate Techniques for Identifying Diffractive Interactions at the LHC ' , International Journal of Modern Physics A , vol. 25 , no. 8 , pp. 1615 . en
dc.identifier.issn 0217-751X
dc.identifier.other PURE: 10135839
dc.identifier.other PURE UUID: 7373f652-b356-451d-8f13-0675f77c08b1
dc.identifier.other ArXiv:
dc.identifier.other WOS: 000276686900005
dc.identifier.other Scopus: 77951673951
dc.description.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. en
dc.format.extent 33
dc.language.iso eng
dc.relation.ispartof International Journal of Modern Physics A
dc.rights en
dc.subject 114 Physical sciences en
dc.subject hep-ex en
dc.subject hep-ph en
dc.title Multivariate Techniques for Identifying Diffractive Interactions at the LHC en
dc.type Article
dc.type.uri info:eu-repo/semantics/other

Files in this item

Total number of downloads: Loading...

Files Size Format View
0909.3039v1 513.6Kb PDF View/Open

This item appears in the following Collection(s)

Show simple item record