标题： 基于人工神经网络的低分辨率恒星光谱 (Teff, log g, [Fe/H]) 分类 显示英文标题

标题： (Teff,log g,[Fe/H]) Classification of Low-Resolution Stellar Spectra using Artificial Neural Networks

摘要： 新一代大视场望远镜、多目标光谱仪以及大格式探测器使得快速获取大量恒星光谱成为可能。在此背景下，传统的逐星谱分析方法已不再实用，需要新的工具能够快速且以合理精度从光谱中提取编码的重要基本恒星参数。近期对应用于天文光谱分类的人工神经网络（ANNs）的分析表明，这一概念能够估算温度和光度。我们已经将 von Hippel 等人（1994年）开发的反向传播ANN技术进行了适应性改进，用于预测分辨率为约2000且信噪比较低的光谱的有效温度、重力加速度及整体金属丰度。我们证明ANN技术在执行三参数（Teff, log g, [Fe/H]）恒星分类方面非常有效。初步结果显示，该技术甚至能够识别训练样本中的异常值。 显示英文摘要

摘要： New generation large-aperture telescopes, multi-object spectrographs, and large format detectors are making it possible to acquire very large samples of stellar spectra rapidly. In this context, traditional star-by-star spectroscopic analysis are no longer practical. New tools are required that are capable of extracting quickly and with reasonable accuracy important basic stellar parameters coded in the spectra. Recent analyses of Artificial Neural Networks (ANNs) applied to the classification of astronomical spectra have demonstrated the ability of this concept to derive estimates of temperature and luminosity. We have adapted the back-propagation ANN technique developed by von Hippel et al. (1994) to predict effective temperatures, gravities and overall metallicities from spectra with resolving power ~ 2000 and low signal-to-noise ratio. We show that ANN techniques are very effective in executing a three-parameter (Teff,log g,[Fe/H]) stellar classification. The preliminary results show that the technique is even capable of identifying outliers from the training sample.