Cascade Training Technique for Particle Identification

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dc.creatorLiu, Yong
dc.creatorStancu, Ion
dc.date2006-11-27
dc.date.accessioned2026-07-07T11:24:07Z
dc.date.available2026-07-07T11:24:07Z
dc.descriptionThe cascade training technique which was developed during our work on the MiniBooNE particle identification has been found to be a very efficient way to improve the selection performance, especially when very low background contamination levels are desired. The detailed description of this technique is presented here based on the MiniBooNE detector Monte Carlo simulations, using both artifical neural networks and boosted decision trees as examples.
dc.description12 pages and 4 EPS figures
dc.identifierhttps://arxiv.org/abs/physics/0611267
dc.identifierhttp://arxiv.org/abs/physics/0611267
dc.identifierNucl.Instrum.Meth.A578:315-321,2007
dc.identifierdoi:10.1016/j.nima.2007.05.173
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/195124
dc.subjectData Analysis, Statistics and Probability
dc.subjectHigh Energy Physics - Experiment
dc.titleCascade Training Technique for Particle Identification
dc.typetext

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