Abstract
International Journal of Advance Research in Multidisciplinary, 2023;1(1):942-947
A study on asteroseismology of sun-like stars using artificial neural networks
Author : Chandan Kumar and Dr. Shailesh Kumar Singh
Abstract
Asteroseismology, the precise determination of star parameters, is essential to many facets of astrophysics. In fact, precise stellar attributes enable us to constrain stellar evolution models more tightly. Moreover, the precision of exoplanet properties is largely dependent on the precision of their host star properties. Finally, defining star populations in the Milky Way and reconstructing its history depend heavily on acquiring precise stellar attributes. It is now widely recognized that the basic star parameters (mass, radius, age, etc.) may be determined with extremely high precision using the low-degree oscillation frequencies in conjunction with the classical observables. An asteroseismic research on 22 of the brightest sun-like stars that Kepler had detected. They were able to deduce the ages to within 2.5% and the masses and radii to an accuracy level of 1.0%. In a comprehensive analysis of the effects of stellar physics uncertainty, conducted a case study on the planet-host star HD52265, which was detected by the CoRoT satellite. They have determined the mass, radius, and age of HD52265 with uncertainties of 7%, 1.5%, and 10%, respectively, after optimizing stellar parameters for a variety of oscillation frequency combinations, large and small frequency separations, frequency ratios, and spectroscopic observables, as well as for various input physics.
Keywords
Asteroseismology, Sun-Like Stars, Artificial Neural Networks