Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/19345
Title: Particle-level kinematic fingerprints and the multiplicity of neutral particles from low-energy strong interactions
Authors: Colecchia, F
Keywords: 29.85.F;high energy physics;particle physics;Large Hadron Collider;LCH;background discrimination;mixture models;latent variable models;sampling;Gibbs sample;Markov Chain Monte Carlo;expectation maximisation
Issue Date: 2014
Publisher: Cornell University
Citation: arXiv:1412.1989 [hep-ph] (17 pp.)
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.
URI: https://bura.brunel.ac.uk/handle/2438/19345
https://arxiv.org/abs/1412.1989
Appears in Collections:Dept of Computer Science Research Papers

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