/ Instrumentation physics/0506202


Electron/pion identification with ALICE TRD prototypes using a neural network algorithm


Adler, C. ; Andronic, A. ; Angelov, V. ; Appelshauser, H. ; Baumann, C. ; Blume, C. ; Braun-Munzinger, P. ; Bucher, D. ; Busch, O. ; Catanescu, V. ; Chernenko, S. ; Ciobanu, M. ; Daues, H. ; Emschermann, D. ; Fateev, O. ; Foka, Y. ; Garabatos, C. ; Glasow, R. ; Gottschlag, H. ; Gunji, T. ; Hamagaki, H. ; Hehner, J. ; Heine, N. ; Herrmann, N. ; Inuzuka, M. ; Kislov, E. ; Lehmann, T. ; Lindenstruth, V. ; Lippmann, C. ; Ludolphs, W. ; Mahmoud, T. ; Marin, A. ; Miskowiec, D. ; Oyama, K. ; Panebratsev, Yu. ; Petracek, V. ; Petrovici, M. ; Radu, A. ; Reygers, K. ; Rusanov, I. ; Sandoval, A. ; Santo, R. ; Schicker, R. ; Simon, R.S. ; Smykov, L. ; Soltveit, H.K. ; Stachel, J. ; Stelzer, H. ; Stockmeier, M.R. ; Tsiledakis, G. ; Verhoeven, W. ; Vulpescu, B. ; Wessels, J.P. ; Windelband, B. ; Yurevich, V. ; Zanevsky, Yu. ; Zaudtke, O.

Pages: 13

Abstract: We study the electron/pion identification performance of the ALICE Transition Radiation Detector (TRD) prototypes using a neural network (NN) algorithm. Measurements were carried out for particle momenta from 2 to 6 GeV/c. An improvement in pion rejection by about a factor of 3 is obtained with NN compared to standard likelihood methods.

Keyword(s): drift chamber ; electron/pion identification ; transition radiation detector ; neural network

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 Record created 2010-10-28, last modified 2014-01-30



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