Direct Photon Identification with Artificial Neural Network in the Photon Spectrometer PHOS

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dc.creatorBogolyubsky, M. Yu.
dc.creatorKharlov, Yu. V.
dc.creatorSadovsky, S. A.
dc.date2004-10-29
dc.date.accessioned2026-07-07T05:53:15Z
dc.date.available2026-07-07T05:53:15Z
dc.descriptionA neural network method is developed to discriminate direct photons from the neutral pion background in the PHOS spectrometer of the ALICE experiment at the LHC collider. The neural net has been trained to distinguish different classes of events by analyzing the energy-profile tensor of a cluster in its eigen vector coordinate system. Monte-Carlo simulations show that this method diminishes by an order of magnitude the probability of $π^0$-meson misidentification as a photon with respect to the direct photon identification efficiency in the energy range up to 120 GeV.
dc.description12 pages, TeX (or Latex, etc), https://edms.cern.ch/document/406291/2
dc.identifierhttps://arxiv.org/abs/physics/0410277
dc.identifierhttp://arxiv.org/abs/physics/0410277
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/86581
dc.subjectInstrumentation and Detectors
dc.subjectData Analysis, Statistics and Probability
dc.titleDirect Photon Identification with Artificial Neural Network in the Photon Spectrometer PHOS
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