Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ

XU Ye; HOU Jian; ZHU Kai-En

doi:10.1088/1674-1137/32/3/008

Chinese Physics C> 2008, Vol. 32> Issue(3) : 201-204 DOI: 10.1088/1674-1137/32/3/008

Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ

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Abstract：
The Monte-Carlo samples of pion, kaon and proton generated from 0.3GeV/c to 1.2GeV/c by the `tester' generator from SIMBES which are used to simulate the detector of BESⅡ are identified with the Bayesian neural networks (BNN). The pion identification and misidentification efficiencies are obviously better at high momentum region using BNN than the methods of χ² analysis of dE/dX and TOF information. The kaon identification and misidentification efficiencies are obviously better from 0.3GeV/c to 1.2GeV/c using BNN than the methods of χ² analysis. The proton identification and misidentification efficiencies using BNN are basically consistent with the ones of χ²analysis. The anti-proton identification and misidentification efficiencies are better below 0.6GeV/c using BNN than the methods of χ²analysis.

References

[1]

. BAI J Z et al. (BES Collaboration). Nucl. Instrum. Methods A, 2001, 458: 6272. Neal R M. Bayesian Learning of Neural Networks. New York: Springer-Verlag, 19963. Beale R, Jackson T. Neural Computing: An Introduction.New York: Adam Hilger, 19914. QIN Hu et al. HEP NP, 2004, 28(7): 738-743 (in Chi-nese)5. Bhat P C, Prosper H P. Bayesian Neural Networks. In:Lyons L, Unel M K ed. Proceedings of Statistical Problems in Particle Physics, Astrophysics and Cosmology, Oxford,UK 12-15, September 2005. London: Imperial college Press.2006. 151-1546. Duane S, Kennedy A D, Pendleton B J et al. Physics Letters B, 1987, 195: 216-2227. Creutz M, Gocksch A. Physical Review Letters, 1989, 63:9-128. Mackenzie P B. Physics Letters B, 1989, 226: 369-3719. Ablikim M et al. (BES Collaboration). Nucl. Instrum. Methods A, 2005, 552: 344

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XU Ye, HOU Jian and ZHU Kai-En. Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ[J]. Chinese Physics C, 2008, 32(3): 201-204. doi: 10.1088/1674-1137/32/3/008

XU Ye, HOU Jian and ZHU Kai-En. Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ[J]. Chinese Physics C, 2008, 32(3): 201-204. doi: 10.1088/1674-1137/32/3/008 shu

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Received: 2007-05-28
Revised: 2007-06-14

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通讯作者: 陈斌, [email protected]

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沈阳化工大学材料科学与工程学院沈阳 110142

Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ

Corresponding author: XU Ye,

Received Date: 2007-05-28

Accepted Date: 2007-06-14

Available Online: 2008-03-05

Abstract:

The Monte-Carlo samples of pion, kaon and proton generated from 0.3GeV/c to 1.2GeV/c by the `tester' generator from SIMBES which are used to simulate the detector of BESⅡ are identified with the Bayesian neural networks (BNN). The pion identification and misidentification efficiencies are obviously better at high momentum region using BNN than the methods of χ² analysis of dE/dX and TOF information. The kaon identification and misidentification efficiencies are obviously better from 0.3GeV/c to 1.2GeV/c using BNN than the methods of χ² analysis. The proton identification and misidentification efficiencies using BNN are basically consistent with the ones of χ²analysis. The anti-proton identification and misidentification efficiencies are better below 0.6GeV/c using BNN than the methods of χ²analysis.

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Applying Bayesian neural networks to identify pion, kaon and proton in BESⅡ

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通讯作者: 陈斌, [email protected]

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