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

Abstract
HTML
Reference
Related

PDF

Abstract：

The Monte-Carlo samples of pion, kaon and proton generated from 0.3GeV/cto 1.2GeV/cby 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 χ²analysis of dE/dXand TOF information. The kaon identification and misidentification efficiencies are obviously better from 0.3GeV/cto 1.2GeV/cusing BNN than the methods of χ²analysis. 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/cusing 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

Access

Get Citation

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

RIS(for EndNote,Reference Manager,ProCite)

BibTex

Txt

Milestone

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

Article Metric

Article Views(5027)
PDF Downloads(706)
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

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/cto 1.2GeV/cby 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 χ²analysis of dE/dXand TOF information. The kaon identification and misidentification efficiencies are obviously better from 0.3GeV/cto 1.2GeV/cusing BNN than the methods of χ²analysis. 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/cusing BNN than the methods of χ²analysis.