利用人工神经网络从恒星光谱预测大气基本参数

Y. A. Azzam, M. Nouh, A. Shaker
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引用次数: 2

摘要

地面和天基仪器的创新将我们带入了光谱学的新时代,在这个时代,大量的恒星内容变得可用。因此,近三十年来,由于大量观测光谱数据库的可用性以及理论光谱的可用性,恒星光谱的自动分类变得主观。本文提出了一种用于恒星光谱自动分类的人工神经网络(ANN)算法。应用该算法提取了斯隆数字巡天(SDSS)观测到的一些热富氦白矮星光谱的基本参数和Onderjove天文台观测到的b型光谱。我们比较了目前的基本参数和最小距离方法的基本参数,以澄清目前算法的准确性,其中我们发现两个样本的预测大气参数在大约50%的样本中是一致的。讨论了对其余样品中发现的差异的可能解释。
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Prediction of the atmospheric fundamental parameters from stellar spectra using artificial neural network
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.
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