Prediction of the atmospheric fundamental parameters from stellar spectra using artificial neural network. Issue 1 (1st January 2021)

• Record Type: Journal Article
• Authors: Azzam, Yosry A.; Nouh, M. I.; Shaker, A. A.
• Is Part Of: NRIAG journal of astronomy and geophysics. Volume 10:Issue 1(2021)
• Page Start: 23
• Page End: 34
• Publication Date: 2021-01-01
• Subjects: Automatic spectral classification -- synthetic spectra -- Artificial Neural Networks -- minimum distance method; Astronomy -- Periodicals; Geophysics -- Periodicals; Astronomy; Geophysics; Electronic journals; Electronic journals; Periodicals; 520.5
• DOI: 10.1080/20909977.2020.1853012
• Languages: English
• ISSNs: 2090-9977

Abstract

ABSTRACT: Innovation in the ground and space-based instruments has taken us into a new age of spectroscopy, in which a large amount of stellar content is becoming available. So, automatic classification of stellar spectra became subjective in the last three decades due to the availability of large observed spectral database as well as the theoretical spectra. In the present paper, we develop an Artificial Neural Network (ANN) algorithm for automated classification of stellar spectra. The algorithm has been applied to extract the fundamental parameters of the optical spectra of some hot helium-rich white dwarf stars observed by the Sloan Digital Sky Survey (SDSS) and B-type spectra observed at Onderjove observatory. We compared the present fundamental parameters and those from a minimum distance method to clarify the accuracy of the present algorithm where we found that the predicted atmospheric parameters for the two samples are in good agreement for about 50% of the samples. A possible explanation for the discrepancies found for the rest of the samples is discussed.