arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2006.14031astro-ph.SRastro-ph.IM
收藏

利用恒星光谱预报大气基本参数的人工神经网络方法

Prediction of the Atmospheric Fundamental Parameters from Stellar Spectra Using Artificial Neural Network

Y. A. Azzam, M. I. Nouh, A. A. Shaker

摘要:

地面和空间仪器的创新将我们带入了一个新的光谱学时代,在这个时代,大量的恒星内容变得可用。由于大量的观测光谱数据和理论光谱数据的存在,近年来恒星光谱的自动分类变得越来越主观。在本文中,我们开发了一个人工神经网络(ANN)算法的恒星光谱自动分类。该算法已被应用于Sloan数字巡天(SDSS)观测到的一些热富氦白矮星和Onderjove天文台观测到的B型光谱的基本参数的提取。我们比较了目前的基本参数和那些从最小距离方法,以澄清本算法的准确性,我们发现,预测的大气参数为两个样本是在良好的协议约50%的样本。一个可能的解释为其余的样品发现的差异进行了讨论。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑