HBT analysis in ALICE with ITS stand-alone and combined neural tracking (preliminary results)

A neural network based algorithm to perform track recognition in the ALICE inner tracking system (ITS) for high transverse momentum particles (p/sub 1/ > 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 la...

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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: 2004
Materias:
Detectors and Experimental Techniques
Acceso en línea:http://cds.cern.ch/record/909025
Descripción
Sumario:A neural network based algorithm to perform track recognition in the ALICE inner tracking system (ITS) for high transverse momentum particles (p/sub 1/ > 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 large track density expected at ALICE. Results are shown for central Pb-Pb events at 5.5 A TeV in the center of mass system and the comparison with the Kalman filter results is included. Data coming from this tracking procedure are used for 1- dimensional HBT correlations and results are presented.

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