POSTER P417: Fundamental parameter estimations of O-type stars binary systems using recurrent neural networks

Theme: AI in Astronomy

pretalxeposter

Recent studies have established O-type stars as predominantly born in binary star systems. In this work, we explore the application of a recurrent neural network system for estimating the effective temperature and surface gravity of binary star systems. Additionally, we assess the neural network's sensitivity in processing synthetic binary spectra derived from two stellar spectra models of O-type stars and the implications of the contribution from the secondary star in the system. Finally, we compare the estimations produced by our proposed system with those from prior research.