Author/Authors :
Pulvirenti، نويسنده , , A. and Badalà، نويسنده , , A. La Barbera، نويسنده , , R. and Lo Re، نويسنده , , G. and Palmeri، نويسنده , , A. and Pappalardo، نويسنده , , G.S. and Riggi، نويسنده , , F.، نويسنده ,
Abstract :
A neural network-based algorithm to perform track recognition in the ALICE Inner Tracking System (ITS) for high transverse momentum particles ( p t > 1 GeV / c ) is presented. The model is based on the Denby–Peterson scheme, especially improved to cope with the very high track density expected in ALICE. Results are shown for central and mid-central Pb–Pb collisions at the CERN Large Hadron Collider energy.
Keywords :
Track finding , Pattern recognition , ALICE experiment , NEURAL NETWORKS
