Statistical Pattern Recognition: Application to $ν_μ\toν_τ$ Oscillation Searches Based on Kinematic Criteria

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dc.creatorBueno, A.
dc.creatorde la Ossa, A. Martinez
dc.creatorNavas, S.
dc.creatorRubbia, A.
dc.date2004-07-01
dc.date.accessioned2026-07-07T11:31:38Z
dc.date.available2026-07-07T11:31:38Z
dc.descriptionClassic statistical techniques (like the multi-dimensional likelihood and the Fisher discriminant method) together with Multi-layer Perceptron and Learning Vector Quantization Neural Networks have been systematically used in order to find the best sensitivity when searching for $ν_μ\to ν_τ$ oscillations. We discovered that for a general direct $ν_τ$ appearance search based on kinematic criteria: a) An optimal discrimination power is obtained using only three variables ($E_{visible}$, $P_{T}^{miss}$ and $ρ_{l}$) and their correlations. Increasing the number of variables (or combinations of variables) only increases the complexity of the problem, but does not result in a sensible change of the expected sensitivity. b) The multi-layer perceptron approach offers the best performance. As an example to assert numerically those points, we have considered the problem of $ν_τ$ appearance at the CNGS beam using a Liquid Argon TPC detector.
dc.description24 pages, 15 figures
dc.identifierhttps://arxiv.org/abs/hep-ph/0407013
dc.identifierhttp://arxiv.org/abs/hep-ph/0407013
dc.identifierJHEP0411:014,2004
dc.identifierdoi:10.1088/1126-6708/2004/11/014
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/197329
dc.subjectHigh Energy Physics - Phenomenology
dc.titleStatistical Pattern Recognition: Application to $ν_μ\toν_τ$ Oscillation Searches Based on Kinematic Criteria
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