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

@inproceedings{Adler2005ElectronpionIW,
  title={Electron/pion identification with ALICE TRD prototypes using a neural network algorithm},
  author={Christian Adler and Harry Arthur Andrews and Venelin Angelov and Harald Appelshaeuser and Claude Baumann and Christian Blume and Peter Braun-Munzinger and Dominik Bucher and Olivier R. Busch and V. Cǎtǎnescu and Sergey Chernenko and Mihaela Grasu Ciobanu and H. W. Daues and D. and Emschermann and Oleg V. Fateev and Yiota Foka and Chilo Garabatos and Richard Glasow and Holger Gottschlag and Solangel Rojas Torres and Hideki Hamagaki and Joerg Hehner and Nicholas Heine and Nobert Herrmann and Masahiro Inuzuka and E. O. Kislov and Th. Lehmann and Volker Lindenstruth and Christian Lippmann and Wilrid Ludolphs and Tahany Mahmoud and Ambroise Marin and Dariusz Czeslaw Miskowiec and Kento Oyama and Yu. A. Panebratsev and Vojtěch Petr{\'a}{\vc}ek and Mihai Petrovici and Alexandru Radu and Klaus Reygers and Igor I. Rusanov and Annette Sandoval and Rainer Santo and Rainer Martin Schicker and Robert Samuel Simon and L P Smykov and Hans Kristian Soltveit and John Stachel and Hermann Stelzer and M. R. Stockmeier and Georgios Karolos Tsiledakis and Wim Verhoeven and S. Altinpinar and Johannes Peter Wessels and Artur Wilk and Bernd Windelband and Vladimir I. Yurevich and Yu. V. Zanevsky and W. A. Zajc},
  year={2005}
}
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. 
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