CAOSP abstracts, Volume: 52, No.: 3, year: 2022

Author(s): Sakan, N.M.; Traparić, I.; Srećković, V.A.; Ivković, M.
Journal: Contributions of the Astronomical Observatory Skalnaté Pleso, vol. 52, no. 3, p. 97-104.
Date: 12/2022
Title: The usage of perceptron, feed and deep feed forward artificial neural networks on the spectroscopy data: astrophysical & fusion plasmas
Keyword(s): Atomic processes, Line: profiles, astrophysical and fusion plasmas
Pages: 97 -- 104
DOI: https://doi.org/10.31577/caosp.2022.52.3.97
NASA ADS Abstract Service: 2022CoSka..52c..97S
Citation:
License: This work is licensed under a Creative Commons Attribution 4.0 International License

Abstract: Artificial neural networks are gaining a momentum for solving complex problems in all sorts of data analysis and classification matters. As such, idea of determining their usability on complex plasma came up. The choice for the input data for the analysis is a set of stellar spectral data. It consists of complex composition plasma under vast variety of conditions, dependent on type of star, measured with calibrated standardized procedures and equipment. The results of the analysis has shown that even a simple type of perceptron artificial neural network could lead to results of acceptable quality for the analysis of spectra of complex composition. The analyzed ANNs performed good on a limited data set. The results can be interpreted as a figure of merit for further development of complex neural networks in various applications e.g. in astrophysical and fusion plasmas.