Electron/Pion Identification with ALICE TRD Prototypes using a Neural Network Algorithm
نویسندگان
چکیده
We study the electron/pion identification performance of the ALICE Transition RaPreprint submitted to Elsevier Science 29 June 2005 diation 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.
منابع مشابه
Electron identification performance withALICETRDprototypes
We present the electron/pion identification performance measured with prototypes for ALICE TRD. Measured spectra of energy deposit of pions and electrons as well as their average values are presented and compared to calculations. Various radiators are investigated over the momentum range of 1 to 6 GeV/c. The time signature of TR is exploited in a bidimensional likelihood mothod.
متن کاملBeam tests with an ALICE TRD supermodule
The Transition Radiation Detector (TRD) of the ALICE experiment at LHC is designed to provide electron/pion identification and tracking of all charged particles [1]. The TRD will supplement the TPC electron/pion identification by a pion rejection factor of the order of 100 at momenta above 1 GeV/c, allowing precision measurements of quarkonia. Sophisticated on-detector electronics [2] is design...
متن کاملPrototype tests for the ALICE TRD
A Transition Radiation Detector (TRD) has been designed to improve the electron identification and trigger capability of the ALICE experiment at the Large Hadron Collider (LHC) at CERN. We present results from tests of a prototype of the TRD concerning pion rejection for different methods of analysis over a momentum range from 0.7 to 2 GeV/c. We investigate the performance of different radiator...
متن کاملHigh efficiency TRD for CBM in test beam and simulation
The CBM (Compressed Baryonic Matter) experiment is designed as a fixed target experiment, in which a TRD shall provide tracking of all charged particles, electron identification and discrimination against a large pion background. In order to fulfill these tasks in the context of high count rates of up to 150 kHz/cm and high particle multiplicities, we constructed TRD prototypes based on a symme...
متن کاملIdentification of Wind Turbine using Fractional Order Dynamic Neural Network and Optimization Algorithm
In this paper, an efficient technique is presented to identify a 2500 KW wind turbine operating in Kahak wind farm, Qazvin province, Iran. This complicated system dealing with wind behavior is identified by using a proposed fractional order dynamic neural network (FODNN) optimized with evolutionary computation. In the proposed method, some parameters of FODNN are unknown during the process of i...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005