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    • Electron/pion Identification with ALICE TRD Prototypes Using a Neural Network Algorithm

    Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detector...（2005）

    Physikaliches Institut der Universität Heidelberg | Gesellschaft für Schwerionenforschung | Kirchhoff-Institut für Physik | Institut für Kernphysik | NIPNE Bucharest | JINR Dubna | University of Tokyo | Univ Munster

    Cited 23|Views42
    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 6GeV/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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    drift chamber,electron/pion identification,transition radiation detector,neural network
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    【要点】：该论文通过使用神经网络算法研究了ALICE过渡辐射探测器（TRD）原型对电子/π介子识别的性能，在2至6GeV/c的粒子动量范围内进行测量，发现相较于标准似然方法，使用神经网络算法能提高π介子的拒绝效率约3倍。
    

    【方法】：研究采用了神经网络算法来进行电子/π介子的识别。
    

    【实验】：实验在2至6GeV/c的粒子动量范围内进行，使用标准似然方法和神经网络算法分别对ALICE TRD原型进行电子/π介子识别，结果显示神经网络算法显著提高了π介子的拒绝效率。
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