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

Abstract
HTML
Reference
Related

PDF

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

[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

[1]	, , . Particle Identification using Artificial Neural Networks at BESIII. Chinese Physics C, 2008, 32(1): 1-8. doi: 10.1088/1674-1137/32/1/001
[2]	GU Qin-Zhong , JIANG Feng-Chun , WANG Xue-Lei , YANG Hua . Probing Top Quark Polarization Effects in Topcolor-Assisted Technicolor Model. Chinese Physics C, 2007, 31(11): 1010-1015.
[3]	WEI Hui-Ling , CHEN Gang . Study on the Characters of Angular Distribution of Quark Jets and Gluon Jets. Chinese Physics C, 2007, 31(10): 907-912.
[4]	CHEN Gang , LIU Lian-Shou . Study of the Hardness Property of Quark and Gluon Jets. Chinese Physics C, 2004, 28(5): 469-474.
[5]	YIN Jian-Wu , LIU Feng , LIU Lian-Shou . Self-Affine Analysis for the Dynamical Fluctuations inside Jets Produced in High Energy Hadron-Hadron Collisions. Chinese Physics C, 2002, 26(2): 152-157.
[6]	ZHANG Kun-Shi , YIN Jian-Wu , CHEN Gang , LIU Chao , LIU Lian-Shou . Monte Carlo Study for the Dynamical Fluctuations Inside a Single Jet in 2-Jet Events. Chinese Physics C, 2002, 26(2): 158-163.
[7]	ZHANG Kun-Shi , CHEN Gang , YU Mei-Ling , LIU Lian-Shou . Monte Carlo Study on the Properties of Gluon and Quark Jets. Chinese Physics C, 2002, 26(11): 1110-1116.
[8]	LU Gong-Ru , YUE Chong-Xing , LI Wei-Bin , SUN Jun-Feng . Charged Top-Pions Corrections to the Top Quark Production at LC in TC2 Theory. Chinese Physics C, 2001, 25(2): 101-107.
[9]	GUO Yuan-Yuan , ZENG Jie , YIN Jian-Wu . Monte Carlo Study of the Dynamical Fluctuation Properties inside Jets in High Energy Hadron-Hadron Collisions. Chinese Physics C, 2001, 25(11): 1084-1088.
[10]	LV Yan , YANG Li-Jian , YANG Li-Ping , MAO Tian . Monte Carlo Study of the Properties of Jets in High Energy Hadron-Hadron Collisions. Chinese Physics C, 2001, 25(11): 1077-1083.
[11]	XIONG ZhaoHua , CHEN HeSheng , LU Liang , LU GongRu . Rare Radiative Decay b→X_sγ in the Technicolor with Scalars Model. Chinese Physics C, 2000, 24(8): 711-718.
[12]	He Zejun . Characteristic Dilepton Distribution for Quark-Gluon Matter. Chinese Physics C, 1997, 21(3): 269-274.
[13]	Liu Ming , Liu Feng(Y) , Liu Feng , Liu Lianshou . On the Methods of Jet Identification in High Energy Collsions. Chinese Physics C, 1997, 21(2): 138-145.
[14]	Liang Hualou , Xie Wei , Ren Jingru , Wang Taijie , Dai Guiliang . Distinguishing Primary Cosmic-Ray Composition with Artificial Neural Networks. Chinese Physics C, 1997, 21(3): 205-210.
[15]	Chen Guoming , Chen Gang . Identifying Tau Hadronic Decay with Neural Network. Chinese Physics C, 1995, 19(8): 692-702.
[16]	Liu Ximing . Difference of Particle Multiplicities Between Quark Jets of Different Flavors in e⁺e^－ Annihilation. Chinese Physics C, 1994, 18(9): 829-835.
[17]	YU You-Wen , SHEN Peng-Nian . STUDIES OF ρ AND K* MESONS DECAY BY THE ONEGLUON-EXCHANGE QUARK-ANTIQUARK PAIR CREATION MODEL. Chinese Physics C, 1988, 12(1): 137-139.
[18]	XIE Qu-Bing , LIU Xi-Ming . QUARK PRODUCTION RULE IN e⁺e^－→TWO JETS EVENTS AT HIGH ENERGY. Chinese Physics C, 1987, 11(6): 834-839.
[19]	DING LIN-KAI , ZHU QING-QI , GUO KE-ZHUN . CLUSTER PHENOMENA OBSERVED IN EMULSION CHAMBER EXPERIMENTS IN ENERGY REGION OF 10¹⁵—10¹⁶ eV AND HADRON JETS WITH LARGE TRANSVERSE MOMENTA. Chinese Physics C, 1982, 6(6): 657-664.
[20]	CHANG CHAO-XI . ON STRUCTURE OF MULTIPARTICLES IN QUARK JETS FOR e⁺e^- COLLIDING. Chinese Physics C, 1982, 6(1): 123-128.

Access

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

RIS(for EndNote,Reference Manager,ProCite)

BibTex

Txt

Milestone

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

Article Metric

Article Views(2930)
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

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.

HTML

Reference (1)

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

Abstract：

References

Access

Article Metrics

Metrics

通讯作者: 陈斌, bchen63@163.com

Email This Article