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

  • The identification of quark and gluon jets produced in eecollisions 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 ee collisions.
  • 加载中
  • [1] . AMY Coll. Kim Y Ket al. Phys. Rev. Lett., 1989, 63: 172. JADE Coll. Bartel W et al. Phys. Lett., 1983, B123: 4603. OPAL Coll. Alexander G et al. Phys. Lett., 1991, B265: 4624. HU Shou-Ren et al. Neural Networks Introduction. Changsha: National University of Defense Technology Publishers, 1993 (in Chinese)(胡守仁等. 神经网络导论. 长沙: 国防科学技术大学出版社, 1993)5. Bishop C M. Neural Networks for Pattern Recognition. Oxford, UK: Oxford University Press, 19956. Ripey B D. Pattern Recognition and Neural Networks. Combridge, UK: Cambridge University Press, 19967. HAN Jia-Wei. Micheline Kamber, Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 20028. Dokshitzer YU L. J. Phys., 1991, G17: 15379. ZHANG Kun-Shi, CHEN Gang, YU Mei-Ling et al. HEP NP, 2002, 26(11): 1110 (in Chinese)(张昆实, 陈刚, 喻梅凌等. 高能物理与核物理, 2002, 26(11): 1110)10. YU Mei-Ling, LIU Lian-Shou. Chin. Phys. Lett, 2002, 19: 647
  • 加载中

Get Citation
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
Milestone
Received: 2004-05-08
Revised: 1900-01-01
Article Metric

Article Views(2708)
PDF Downloads(626)
Cited by(0)
Policy on re-use
To reuse of subscription content published by CPC, the users need to request permission from CPC, unless the content was published under an Open Access license which automatically permits that type of reuse.
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Email This Article

Title:
Email:

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

Abstract: The identification of quark and gluon jets produced in eecollisions 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 ee collisions.

    HTML

Reference (1)

目录

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return