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

Autor:	Abdelkader Guesmia, Mohamed E. A. Benamar, Brahim Mohamedi, Brahim Beladel
Rok vydání:	2018
Předmět:	010302 applied physics Artificial neural network Chemistry 0103 physical sciences X-ray Analytical chemistry Production (computer science) 02 engineering and technology Physical and Theoretical Chemistry 021001 nanoscience & nanotechnology 0210 nano-technology 01 natural sciences L-shell
Zdroj:	Radiochimica Acta. 106:1017-1021
ISSN:	2193-3405 0033-8230
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::446092833a693ad4931bde1f95dced7c https://doi.org/10.1515/ract-2018-2990 Zobrazit plný text záznamu