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Year 2013, Volume: 34 Issue: 1, 42 - 51, 23.01.2013

Serkan Akkoyun , Tuncay Bayram , Seyit Kara

Abstract

References

  • Agostinelli, S., et al., 2003. Geant4-A simulation toolikit. Nucl. Instr. Meth. Phys. Res. A 506, 250-303.
  • Akkoyun, S., et al., 2012. AGATA-Advanced GAmmaTrackingArray. Nucl. Instr. Meth. Phys. Res. A 668, 26-58.
  • Schmid, G.J., et al., 1999. A Gamma-ray tracking algorithm for the GRETA Spectrometer. Nucl. Instr. Meth. Phys. Res. A 430, 69-83.
  • Ataç, A,. et al., 2009. Discrimination of Gamma-rays Due to Inelastic Neutron Scattering in AGATA. Nucl. Instr. Meth. Phys. Res. A 607 554-563.
  • Cao, Z., et al., 1998. Implementation of dynamic bias for neutron–photon pulse shape discrimination by using neural network classifiers. Nucl. Instr. Meth. Phys. Res. A 416, 4384
  • Esposito, B., Fortuna, L., Rizzo, A., 2004. Neural neutron/gamma discrimination in organic scintillators for fusion applications. IEEE International Joint Conference on, Volume: 4, 293129
  • Liu, G., et al., 2009. An investigation of the digital discrimination of neutrons and γ rays with organic scintillation detectors using an artificial neural network. Nucl. Instr. Meth. Phys. Res. A 607, 620-628.
  • Yildiz, N and Akkoyun, S., 2013. Neural network consistent empirical physical formula construction for neutron–gamma discrimination in gamma ray tracking. Annals of Nucl. Energy. 51, 10-17.
  • Vetter, K., 2001. GRETA: The proof-of-principle for gamma-ray tracking. Nucl. Phys. A 682, 286-294.
  • GSI web site: http://www.gsi.de. Akkoyun, S and Yildiz, N., 2012. Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors by using artificial neural networks. Rad. Meas. 47, 571-5
  • Bazzacco, D., 2004. The Advanced Gamma Ray Tracking Array AGATA. Nucl. Phys.A 746, 248-254.
  • Van der Marel, J., Cederwall, B., 1999. Backtracking as a Way to Reconstruct Compton Scattered Gamma-rays. Nucl. Instr. Meth. Phys. Res. A 437, 538-551.
  • Haykin, S., 1999. Neural networks: a comprehensive foundation, 2nd ed, Prentice-Hall, New Jersey.
  • Medhat, M.E., 2012. Artificial intelligence methods applied for quantitative analysis of natural radioactive sources. Annals of Nucl. Energy. 45, 73-79.
  • Levenberg, K., 1944. A method for the solution of certain nonlinear problems in least squares. Quart. Appl. Math. 2, 164.
  • Marquardt, D., 1963. An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431.

Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks

Year 2013, Volume: 34 Issue: 1, 42 - 51, 23.01.2013

Serkan Akkoyun , Tuncay Bayram , Seyit Kara

Abstract

The neutrons emitted in heavy-ion fusion-evaporation (HIFE) reactions together with the gamma-rays cause unwanted backgrounds in gamma-ray spectra. Especially in the nuclear reactions where relativistic ion beams (RIBs) are used, these neutrons are serious problem. They have to be rejected in order to obtain clearer gamma-ray peaks. In this study, the radiation energy and three criteria which are previously determined for separation of neutron and gamma-rays in the HPGe detectors have been used in artificial neural network (ANN) for improving of the decomposition power. According to the preliminary results, by the help of ANN method, the ratio of neutron rejection has been improved by a factor of 1.27 and the ratio of the lost in gamma-rays has been decreased by a factor of 0.5.

Keywords

HPGe detectors gamma-ray tracking artificial neural network Geant4 neutron-gamma separation

References

  • Agostinelli, S., et al., 2003. Geant4-A simulation toolikit. Nucl. Instr. Meth. Phys. Res. A 506, 250-303.
  • Akkoyun, S., et al., 2012. AGATA-Advanced GAmmaTrackingArray. Nucl. Instr. Meth. Phys. Res. A 668, 26-58.
  • Schmid, G.J., et al., 1999. A Gamma-ray tracking algorithm for the GRETA Spectrometer. Nucl. Instr. Meth. Phys. Res. A 430, 69-83.
  • Ataç, A,. et al., 2009. Discrimination of Gamma-rays Due to Inelastic Neutron Scattering in AGATA. Nucl. Instr. Meth. Phys. Res. A 607 554-563.
  • Cao, Z., et al., 1998. Implementation of dynamic bias for neutron–photon pulse shape discrimination by using neural network classifiers. Nucl. Instr. Meth. Phys. Res. A 416, 4384
  • Esposito, B., Fortuna, L., Rizzo, A., 2004. Neural neutron/gamma discrimination in organic scintillators for fusion applications. IEEE International Joint Conference on, Volume: 4, 293129
  • Liu, G., et al., 2009. An investigation of the digital discrimination of neutrons and γ rays with organic scintillation detectors using an artificial neural network. Nucl. Instr. Meth. Phys. Res. A 607, 620-628.
  • Yildiz, N and Akkoyun, S., 2013. Neural network consistent empirical physical formula construction for neutron–gamma discrimination in gamma ray tracking. Annals of Nucl. Energy. 51, 10-17.
  • Vetter, K., 2001. GRETA: The proof-of-principle for gamma-ray tracking. Nucl. Phys. A 682, 286-294.
  • GSI web site: http://www.gsi.de. Akkoyun, S and Yildiz, N., 2012. Consistent empirical physical formula construction for recoil energy distribution in HPGe detectors by using artificial neural networks. Rad. Meas. 47, 571-5
  • Bazzacco, D., 2004. The Advanced Gamma Ray Tracking Array AGATA. Nucl. Phys.A 746, 248-254.
  • Van der Marel, J., Cederwall, B., 1999. Backtracking as a Way to Reconstruct Compton Scattered Gamma-rays. Nucl. Instr. Meth. Phys. Res. A 437, 538-551.
  • Haykin, S., 1999. Neural networks: a comprehensive foundation, 2nd ed, Prentice-Hall, New Jersey.
  • Medhat, M.E., 2012. Artificial intelligence methods applied for quantitative analysis of natural radioactive sources. Annals of Nucl. Energy. 45, 73-79.
  • Levenberg, K., 1944. A method for the solution of certain nonlinear problems in least squares. Quart. Appl. Math. 2, 164.
  • Marquardt, D., 1963. An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431.
There are 16 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Editorial
Authors

Serkan Akkoyun

Tuncay Bayram

Seyit Kara

Publication Date January 23, 2013
Published in Issue Year 2013 Volume: 34 Issue: 1

Cite

APA Akkoyun, S., Bayram, T., & Kara, S. (2013). Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, 34(1), 42-51.
AMA Akkoyun S, Bayram T, Kara S. Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. April 2013;34(1):42-51.
Chicago Akkoyun, Serkan, Tuncay Bayram, and Seyit Kara. “Improvement Studies on Neutron-Gamma Separation in HPGe Detectors by Using Neural Networks”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 34, no. 1 (April 2013): 42-51.
EndNote Akkoyun S, Bayram T, Kara S (April 1, 2013) Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 34 1 42–51.
IEEE S. Akkoyun, T. Bayram, and S. Kara, “Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks”, Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, vol. 34, no. 1, pp. 42–51, 2013.
ISNAD Akkoyun, Serkan et al. “Improvement Studies on Neutron-Gamma Separation in HPGe Detectors by Using Neural Networks”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi 34/1 (April 2013), 42-51.
JAMA Akkoyun S, Bayram T, Kara S. Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2013;34:42–51.
MLA Akkoyun, Serkan et al. “Improvement Studies on Neutron-Gamma Separation in HPGe Detectors by Using Neural Networks”. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi, vol. 34, no. 1, 2013, pp. 42-51.
Vancouver Akkoyun S, Bayram T, Kara S. Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks. Cumhuriyet Üniversitesi Fen Edebiyat Fakültesi Fen Bilimleri Dergisi. 2013;34(1):42-51.