On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method

ZHANG Kun-Shi; LIU Lian-Shou

Chinese Physics C> 2004, Vol. 28> Issue(11) : 1141-1145

On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method

ZHANG Kun-Shi ,
LIU Lian-Shou ^,

Institute of Particle Physics,Huazhong Normal University,Wuhan 430079,China2 School of Physics Science and Technology,Yangtze University,Jingzhou 434020,China

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Abstract：
The identification of quark and gluon jets produced in e^＋e^－collisions using the artificial neural network method is addressed.The structure and the learning algorithm of the BP(Back Propagation)neural network model is studied.Three characteristic parameters—the average multiplicity and the average transverse momentum of jets and the average value of the angles opposite to the quark or gluon jets are taken as training parameters and are inputed to the BP network for repeated training.The learning process is ended when the output error of the neural network is less than a pre-set precision(σ=0.005).The same training routine is repeated in each of the 8 energy bins ranging from 2.5—22.5 GeV,respectively.The finally updated weights and thresholds of the BP neural network are tested using the quark and gluon jet samples,getting from the non-symmetric three-jet events produced by the Monte Carlo generator JETSET 7.4.Then the pattern recognition of the mixed sample getting from the combination of the quark and gluon jet samples is carried out through applying the trained BP neural network.It turns out that the purities of the identified quark and gluon jets are around 75%—85%,showing that the artificial neural network is effective and practical in jet analysis.It is hopeful to use the further improved BP neural network to study the experimental data of high energy e^＋e^－ collisions.

References

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ZHANG Kun-Shi and LIU Lian-Shou. On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method[J]. Chinese Physics C, 2004, 28(11): 1141-1145.

ZHANG Kun-Shi and LIU Lian-Shou. On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method[J]. Chinese Physics C, 2004, 28(11): 1141-1145. shu

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Received: 2004-05-08
Revised: 1900-01-01

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沈阳化工大学材料科学与工程学院沈阳 110142

On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method

Corresponding author: LIU Lian-Shou,

Institute of Particle Physics,Huazhong Normal University,Wuhan 430079,China2 School of Physics Science and Technology,Yangtze University,Jingzhou 434020,China

Received Date: 2004-05-08

Accepted Date: 1900-01-01

Available Online: 2004-11-05

Abstract: The identification of quark and gluon jets produced in e^＋e^－collisions using the artificial neural network method is addressed.The structure and the learning algorithm of the BP(Back Propagation)neural network model is studied.Three characteristic parameters—the average multiplicity and the average transverse momentum of jets and the average value of the angles opposite to the quark or gluon jets are taken as training parameters and are inputed to the BP network for repeated training.The learning process is ended when the output error of the neural network is less than a pre-set precision(σ=0.005).The same training routine is repeated in each of the 8 energy bins ranging from 2.5—22.5 GeV,respectively.The finally updated weights and thresholds of the BP neural network are tested using the quark and gluon jet samples,getting from the non-symmetric three-jet events produced by the Monte Carlo generator JETSET 7.4.Then the pattern recognition of the mixed sample getting from the combination of the quark and gluon jet samples is carried out through applying the trained BP neural network.It turns out that the purities of the identified quark and gluon jets are around 75%—85%,showing that the artificial neural network is effective and practical in jet analysis.It is hopeful to use the further improved BP neural network to study the experimental data of high energy e^＋e^－ collisions.

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On the Identification of Quark and Gluon Jets Using Artificial Neural Network Method

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