Determination of saturation depth in Compton scattering using Artificial Neural Network

Ashrafi, S; Alizadeh, D; Jahanbakhsh, O

doi:10.29252/ijpr.18.2.195

Determination of saturation depth in Compton scattering using Artificial Neural Network

Authors

S Ashrafi

D Alizadeh

O Jahanbakhsh

https://doi.org/10.29252/ijpr.18.2.195

Abstract

Abstract:

The intensity of Compton scattered γ-ray photons provide useful information about the electron density distribution of a test sample. Because of photon attenuation, the application of this method is limited to a certain depth of the sample (saturation depth). The saturation depth value depends on the energy and intensity of primary photons and on the material of the sample. In this study, we measured the energy spectrum of the scattered photons of 662 keV at 90° with a NaI(Tl) scintillator; and determined the saturation depth of the sample by the Artificial Neural Network (ANN) algorithm. Two sets of samples with known and unknown density were used to train and test the network, respectively. The highest precision (with 0.15% relative error) was achieved by using the Levenbert-Marquardt algorithm with five hidden layers.

Keywords

Compton scattering

saturation depth

artificial neural networks

Levenbert-Marquardt algorithm

[1] A.D. Sabharwal, B.S. Sandhu, “Radiat. Meas”. 44, (2009), 411-414.
[2] G.F. Knoll, "Radiation Detection and Measurement," John Wiley & Sons, (2010).
[3] J. Wang, Y. Wang, Z. Chi. “IEE Proc.-Sci. Meas. Technol”. 146, (1999), 235 – 239.
[4] I.L.M Silva, R.T Lopes, E.F.O deJesus. “Nucl. Instrum. Methods Phy. Rese. A”. 422, (1999), 957-963.
[5] M. Singh, G. Singh, B.S. Sandhu. “Appl. radiat. Isot”. 64, (2006), 373-378.
[6] A.D. Sabharwal, M. Singh, B. Singh, B.S. Sandhu, “Appl. radiat. Isot”. 66, (2008), 1467-1473.
[7] S. Ashrafi, O. Jahanbakhsh, D. Alizadeh; “Nucl. Instrum. Methods Phy. Rese. A”. 760 (2014), 1–4.
[8] N. Shengli, et al.; Proceeding of the Second International Workshop on EGS, 8-12. August 2000.
[9] N. Tsoulfanidis, ‘Measurements and Detection of Radiation’, CRC Press, Taylor & Francis Group, LLC. 2015.
[10] F.A. Balogun, N.M Spyrou; “Nucl. Instrum. Methods Phy. Rese. B” 83, (1993), 533-538.
[11] M.T. Hagan, B. DEMUTH. “Neural Network Design” Mark Beale MHB, Inc.PWS Publishing Company, 2000.
[12] F. Rosenblatt, "Principles of Neurodynamics," Spartan Press, Washington D.C. 1961.
[13] J. Wu, et al. “Energ. Buildings”. 43, (2011), 1685–1693.
[14] ATOMTEX Corporation Gamma-Beta-Radiation Spectrometer AT1315, User manual. (1998). http://www.atomtex.com.
[15] M. Hosoz, H.M. Ertunc, H. Bulgurcu, “Energy Convers. Manage”. 48, (2007), 1349–1359.
[16] MATLAB 7 "Neural network tool box user's guide", Math Works Inc, 2009.
[17] T.P. Vogl, J.K. Mangis, A.K. Rigler, W.T. Zink, D.L. Alkon, “Biol. Cybern”. 59, (1988), 257–263.