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

被引：11

作者：

Adler, C

Andronic, A

Angelov, V

Appelshäuser, 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, Y

Petracek, V

Petrovici, M

Radu, A

Reygers, K

Rusanov, I

Sandoval, A

Santo, R

Schicker, R

Simon, RS

Smykov, L

Soltveit, HK

Stachel, J

Stelzer, H

Stockmeier, MR

Tsiledakis, G

机构：

[1] Univ Munster, Inst Kernphys, D-4400 Munster, Germany

[2] Univ Heidelberg, Inst Phys, D-6900 Heidelberg, Germany

[3] Gesell Schwerionenforsch mbH, D-6100 Darmstadt, Germany

[4] Kirchhoff Inst Phys, Heidelberg, Germany

[5] Univ Frankfurt, Inst Kernphys, D-6000 Frankfurt, Germany

[6] NIPNE, Bucharest, Romania

[7] JINR, Dubna, Russia

[8] Univ Tokyo, Tokyo, Japan

来源：

NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT | 2005年 / 552卷 / 03期

关键词：

drift chamber; electron/pion identification; transition radiation detector; neural network;

D O I：

10.1016/j.nima.2005.07.006

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

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. (c) 2005 Elsevier B.V. All rights reserved.

引用

页码：364 / 371

页数：8

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