Neural network prediction of K and L-shell X-ray production cross sections-Reference-Cited by-同舟云学术

Neural network prediction of K and L-shell X-ray production cross sections

Published:2018-08-30 Issue:12 Volume:106 Page:1017-1021
ISSN:2193-3405
Container-title:Radiochimica Acta
language:en
Short-container-title:

Author:

Beladel Brahim¹,Mohamedi Brahim²,Guesmia Abdelkader³,Benamar Mohamed E. A.³

Affiliation:

1. Department of Physics , Ziane Achour University Djelfa , Djelfa , Algeria

2. Nuclear Research Center of Birine , Djelfa , Algeria

3. FUNDAPL Laboratory, Saad Dahlab University Blida 1 , Blida , Algeria

Abstract

Abstract The ionization and X-ray production cross section are fundamental parameters in elemental analysis by PIXE technique. Unfortunately no exact general analytical expression exists, from which the interest of this work. In this paper, we apply the neural network technique in the evaluation of the X-ray production cross sections. The calculations are based on Mukoyama’s PWBA data. Our results are compared with experimental data for protons and alpha particles for energies ranging from hundreds KeV to tens MeV.

Publisher

Walter de Gruyter GmbH

Subject

Physical and Theoretical Chemistry

Link

https://www.degruyter.com/document/doi/10.1515/ract-2018-2990/pdf

Reference14 articles.

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3. Basbas, G., Brandt, W., Laubert, R.: Universal cross sections for K-shell ionization by heavy charged particles. I. Low particle velocities. Phys. Rev. A 7, 983 (1973)

4. Basbas, G., Brandt, W., Ritchie, R. H.: Perturbed-stationary-state theory of atomic inner-shell ionization by heavy charged particles. Phys. Rev. A 7, 1971 (1973).10.1103/PhysRevA.7.1971

5. Brandt, W., Lapicki, G.: Binding and Coulomb-deflection effects in L-shell Coulomb ionization by heavy charged particles. Low particle velocities. Phys. Rev. A 10, 474 (1974).

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