论文标题
使用人工神经网络对恒星光谱的大气基本参数的预测
Prediction of the Atmospheric Fundamental Parameters from Stellar Spectra Using Artificial Neural Network
论文作者
论文摘要
地面和太空仪器的创新使我们进入了新的光谱时代，其中大量恒星含量正在可用。因此，由于大量观察到的光谱数据库以及理论光谱的可用性，近年来，恒星光谱的自动分类变得主观。在本文中，我们开发了一种人工神经网络（ANN）算法，用于自动分类恒星光谱。该算法已应用于提取Sloan Digital Sky Survey（SDSS）（SDSS）和在Onderjove天文台观察到的一些热氦富的白色矮人星的基本参数。我们比较了当前的基本参数和最低距离方法的参数，以阐明当前算法的准确性，在那里我们发现，这两个样品的预测大气参数非常一致，约有50％的样品。讨论了对其他样品发现的差异的可能解释。
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 recent years 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 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.
