Neural tracking in ALICE

A neural network based algorithm to perform track recognition in the ALICE Inner Tracking System (ITS) for high transverse momentum particles (p//t greater than 1 GeV/c) is presented,. The model is based on the Denby-Peterson scheme, with some original improvements which are necessary to cope with t...

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Detalles Bibliográficos
Autores principales: Badalà, A, Barbera, R, Lo Re, G, Palmeri, A, Pappalardo, G S, Pulvirenti, A, Riggi, F
Lenguaje:eng
Publicado: 2003
Materias:
Detectors and Experimental Techniques
Acceso en línea:https://dx.doi.org/10.1016/S0168-9002(03)00482-0
http://cds.cern.ch/record/624971
Descripción
Sumario:A neural network based algorithm to perform track recognition in the ALICE Inner Tracking System (ITS) for high transverse momentum particles (p//t greater than 1 GeV/c) is presented,. The model is based on the Denby-Peterson scheme, with some original improvements which are necessary to cope with the very high track density expected in ALICE. Results are shown for a central Pb-Pb event at 5.5 A TeV in the center of mass system and the comparison with the Kalman Filter results is included.

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