Stark spectral line broadening modeling by machine learning algorithms

Abstract

Various types of electric fields contained in the laboratory and astrophysical plasma cause a Stark broadening of spectral lines in plasma. Therefore, a large number of spectroscopic diagnostics of laboratory and astrophysical plasma are based on experimental and theoretical studies of Stark broadening of spectral lines in plasma. The topic of the present investigation is the Stark broadening caused by free electrons in plasma and its dependence on certain atomic parameters using a new method based on the machine learning (ML) approach. Analysis of empirical data on atomic parameters was done by ML algorithms with more success that it was previously done by classical methods of data analysis. The correlation parameter obtained by artificial intelligence (AI) is slightly better than the one obtained by classical methods, but the scope of application is much wider. AI conclusions are applicable to any physical system while conclusions made by classical analysis are applicable only to a small portion of these systems. ML algorithms successfully identified quantum nature by analyzing atomic parameters. The biggest issue of classical analysis, which is infinite spectral line broadening for high ionization stages, was resolved by AI with a saturation tendency.