Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19345
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dc.contributor.authorColecchia, F-
dc.date.accessioned2019-10-18T13:35:31Z-
dc.date.available2014-12-05-
dc.date.available2019-10-18T13:35:31Z-
dc.date.issued2014-
dc.identifier.citationarXiv:1412.1989 [hep-ph] (17 pp.)en_US
dc.identifier.urihttps://bura.brunel.ac.uk/handle/2438/19345-
dc.identifier.urihttps://arxiv.org/abs/1412.1989-
dc.description.abstract[arXiv] The contamination, or background, from uninteresting low-energy strong interactions is a major issue for data analysis at the Large Hadron Collider. In the light of the challenges associated with the upcoming higher-luminosity scenarios, methods of assigning weights to individual particles have recently started to be used with a view to rescaling the particle four-momentum vectors. We propose a different approach whereby the weights are instead employed to reshape the particle-level kinematic distributions in the data. We use this method to estimate the number of neutral particles originating from low-energy strong interactions in different kinematic regions inside individual collision events. Given the parallel nature of this technique, we anticipate the possibility of using it as part of particle-by-particle event filtering procedures at the reconstruction level at future high-luminosity hadron collider experiments.en_US
dc.format.extent? - ? (17)-
dc.language.isoenen_US
dc.publisherCornell Universityen_US
dc.subject29.85.Fen_US
dc.subjecthigh energy physicsen_US
dc.subjectparticle physicsen_US
dc.subjectLarge Hadron Collideren_US
dc.subjectLCHen_US
dc.subjectbackground discriminationen_US
dc.subjectmixture modelsen_US
dc.subjectlatent variable modelsen_US
dc.subjectsamplingen_US
dc.subjectGibbs sampleen_US
dc.subjectMarkov Chain Monte Carloen_US
dc.subjectexpectation maximisationen_US
dc.titleParticle-level kinematic fingerprints and the multiplicity of neutral particles from low-energy strong interactionsen_US
dc.typeArticleen_US
dc.relation.isPartOfarXiv-
pubs.publication-statusUnpublished-
Appears in Collections:Dept of Computer Science Research Papers

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