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

• Published in: Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment Vol. 552; no. 3; pp. 364 - 371
• Main Authors: Adler, C., Andronic, A., Angelov, V., Appelshäuser, H., Baumann, C., Blume, C., Braun-Munzinger, P., Bucher, D., Busch, O., Cătănescu, 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., Wilk, A., Windelband, B., Yurevich, V., Zanevsky, Yu, Zaudtke, O.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2005
• Subjects: Drift chamber; Electron/pion identification; Neural network; Transition radiation detector; Transition radiation detector; 29.40.Cs; Drift chamber; Neural network; 29.40.Gx; Electron/pion identification
• ISSN: 0168-9002; 1872-9576
• DOI: 10.1016/j.nima.2005.07.006

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.