红团簇巨星的光谱分析及其作为I、K波段标准烛光的研究

基于高分辨率、高信噪比光谱观测资料，确定了19颗贫金属红团簇巨星的恒星大气参数，得到样本星4种α族元素(O、Mg、Ca、Si)的化学丰度．讨论了铁丰度与恒星大气参数的相关性以及α族元素丰度随金属丰度的变化，计算了共58颗红团簇巨星在I、K波段的绝对星等，讨论了它们与恒星铁丰度之间的关系．结果表明，在分析铁丰度范围内相对于I波段，K波段的绝对星等与铁丰度的相关性更弱，并且与利用理论模型得到的红团簇巨星I、K波段绝对星等与铁丰度的关系进行了比较与验证。     

恒星大气物理参量的非参数估计方法

恒星大气物理参量（有效温度、表面重力、化学丰度）是导致恒星光谱差异的主要因素．恒星大气物理参量的自动测量是LAMOST等大规模巡天望远镜所产生的海量天体光谱数据自动处理中一个重要研究内容．针对测量大样本的恒星光谱数据估计每个恒星的大气物理参量，提出了一种基于变窗宽核函数的估计算法：变窗宽算法是对固定窗宽算法的改进，分为3个步骤：（1）将历史恒星光谱数据进行PCA处理，得到光谱的低维特征数据；（2）利用特征数据与其物理参数的对应关系，建立一种变窗宽的非参数估计模型；（3）利用该估计模型，直接计算待测恒星光谱的3个物理参量（有效温度、表面重力、金属丰度）．实验结果表明：该方法与固定窗宽估计模型以及在其他文献中报道的方法相比，具有较高的估计精度和鲁棒性．     

恒星大气参数测量

从恒星的研究意义谈起,介绍了恒星大气参数的基本概念及研究意义;阐述了恒星参数测量方法的分类:直接测量和间接测量。着重评述了间接测量方法,包括测光方法、红外流量方法、巴尔默线轮廓拟合、谱线比例方法、线指数方法、金属线诊断法、光谱模板拟合和机器学习方法等。指出在大型巡天数据中光谱模板拟合与机器学习方法的优势及其广泛应用。对于高分辨率光谱,金属线诊断仍然备受天文学家青睐;红外流量方法的测量结果常用来定标。     

LAMOST-Kepler视场中富锂巨星表面转动研究

Kepler卫星提供的长时序、高精度的光度观测和郭守敬望远镜(LAMOST)提供的大规模光谱观测为研究恒星表面转动周期与富锂巨星锂丰度关系提供了良好的数据.将LAMOST搜寻到的富锂巨星与Kepler观测交叉,获得了619颗共同源,研究了其中295颗有良好观测数据的富锂巨星的表面转动.在205颗有星震学参数的恒星中提取出14颗恒星的转动周期,其中氦核燃烧星(HeB) 11颗,红巨星支(RGB) 2颗, 1颗演化阶段未确定.本样本中的极富锂巨星(A(Li) 3.3 dex)皆为HeB;对于90颗没有星震学参数的样本因而没有依靠星震学手段确定演化阶段的恒星中,有22颗提取出了自转周期.前者的自转探测率为6.8%,显著高于之前工作中大样本巨星2.08%的探测率.同时,此研究首次从自转周期的角度确认了恒星转动与巨星锂增丰存在相关性,在增丰程度较弱时,自转周期分布比较弥散;强锂增丰的星倾向于快速转动.富锂巨星与极富锂巨星在转动速度随锂丰度的演化上展现了两个序列,在转动-锂丰度图上的A(Li)≈3.3 dex处产生第2个下降序列,或许暗示了两者在形成机制上的不同.极富锂巨星的样本中,随巨星锂增丰程度增强,恒星转速加快.这种相关性为由转动引起的额外混合作为富锂巨星形成的机制提供了支持.     

贫金属星的重元素丰度——Ⅰ.观测数据和大气模型

本文给出了27颗贫金属矮星和亚巨星的高分辨率高信噪比光谱观测资料.确定了这些样本星的恒星大气参数.利用Magain的定标方法,从b—y和V—K包指数导出有效温度.由迫使Fell谱线与高激发态Fel谱线给出相同铁丰度值确定表面重力.由Stromgren m_1色指数确定金属丰度[Fe/H].     

基于卷积神经网络的恒星光谱型和光度型的分类模型

恒星光谱分类是天文学中一个重要的研究问题.对于已经采集到的海量高维恒星光谱数据的分类,采用模式匹配方法对光谱型分类较为成功,但其缺点在于标准恒星模版之间的差异性在匹配实际观测数据中不能体现出来,尤其是当需要进行光谱型和光度型的二元分类时模版匹配法往往会失败.而采用谱线特征测量的光度型分类强烈地依赖谱线拟合的准确性.为了解决二元分类的问题,介绍了一种基于卷积神经网络的恒星光谱型和光度型分类模型(Classification model of Stellar Spectral type and Luminosity type based on Convolution Neural Network, CSSL CNN).这一模型使用卷积神经网络来提取光谱的特征,通过注意力模块学习到了重要的光谱特征,借助池化操作降低了光谱的维度并压缩了模型参数的数量,使用全连接层来学习特征并对恒星光谱进行分类.实验中使用了大天区面积多目标光纤光谱天文望远镜(Large Sky Area Multi-Object Fiber Spectroscopy Telescope, LAMOST)公开数据集Data Release 5 (DR5,用了其中71282条恒星光谱数据,每条光谱包含了3000多维的特征)对该模型的性能进行验证与评估.实验结果表明,基于卷积神经网络的模型在恒星的光谱型分类上准确率达到92.04%,而基于深度神经网络的模型(Celestial bodies Spectral Classification Model, CSC Model)只有87.54%的准确率; CSSL CNN在恒星的光谱型和光度型二元分类上准确率达到83.91%,而模式匹配方法MKCLASS仅有38.38%的准确率且效率较低.     

富锂巨星研究概述

锂(Li)元素最初诞生于大爆炸核合成,是最重要的轻元素之一.但锂元素丰度在很多类天体中均表现出观测与理论不符的现象,这一问题困扰了天体物理学家数十年.富锂巨星就是这样的一类天体,它们大气中的Li丰度超过了标准恒星演化模型的理论值.虽然富锂巨星早在约四十年前就被发现,但其起源依然是未解之谜.随着以郭守敬望远镜(LAMOST)巡天等为代表的大型光谱巡天项目的开展、以开普勒(Kepler)卫星为代表的星震学观测数据的产出以及数据驱动类方法和技术的飞速发展,针对富锂巨星的研究取得了一系列重要的突破.在此将回顾富锂巨星近四十年来的研究进展,并总结对于富锂巨星最新的认知.     

SAGE巡天介绍Ⅱ——恒星大气参数估计

主要介绍SAGE巡天的恒星大气参数计算方法。首先回顾了前人利用恒星颜色确定恒星大气参数的工作;然后介绍了确定参数的多项式拟合和深度学习两种方法,并对每一种方法的原理、误差和特点进行了详细描述;最后对利用SAGE巡天恒星大气参数的前景进行展望。     

Mg超丰恒星的性质及其起源的研究

Mg超丰恒星([Mg/Fe]1.0)的特殊丰度模式无法用普通恒星的Mg元素起源和银河系化学演化机制解释。对这类特殊天体的起源和演化及化学丰度性质的研究,有助于深化理解恒星核合成及星系演化中一些特殊过程。首先介绍了目前文献中由高分辨率光谱证认的Mg超丰恒星,并对这些恒星的大气参数、运动学参数和化学丰度特征等性质及其起源机制进行了分析。其次统计了在斯隆巡天数据中系统搜寻的Mg超丰恒星候选体的大气参数和运动学分布特征,并且筛选了其中C超丰的候选体。研究发现绝大部分Mg超丰恒星表现了C超丰;在Mg超丰恒星中,存在中子俘获元素超丰的那些恒星都存在于双星系统中,其演化过程受到了AGB伴星的影响;而没有表现中子俘获元素超丰的那些恒星极有可能起源于第一代低能量超新星,部分恒星具有很高的空间速度,这类空间速度大于300 km/s的Mg超丰恒星可能是搜寻第一代恒星([Fe/H]-5.0)的最好样本。     

基于SDSS的河内恒星DIBs自动识别方法

星际弥散带(Diffuse Interstellar Bands,DIBs)自发现以来已经经历了近百年的研究,但是至今仍然是天体光谱学上的一个未解之谜.针对SDSS DR7的光谱数据提出了一种星际弥散带特征自动识别方法.该方法基于谱线特征匹配,通过光谱流量限制的方法进行星际弥散带特征的自动识别.利用它可对相对定标的巡天光谱进行广泛的星际弥散带候选天体搜索,在海量光谱数据中获取更多具有星际弥散带特征的河内恒星.通过对SDSS DR7中位置相对合适的超过300个盘的天体光谱的遍历,已经得到了一系列具有星际弥散带特征的候选河内恒星,并且证明了该方法简单有效且具有鲁棒性.这为载体证认等工作提供了大量辅助数据,极大地推进了星际弥散带的研究.     

The Cannon: A Data Driving Approach to Stellar Label Determination

The new generation of large sky area spectroscopic survey project has produced nearly 10 million low-resolution stellar spectra. Based on these spectroscopic data, this paper introduces a machine learning algorithm named The Cannon. This algorithm is completely based on the known spectroscopic data of stellar atmospheric parameters (effective temperature, surface gravity, and metal abundance, etc.), this algorithm builds the characteristic vector by means of data driving, and establishes the functional relation between spectral flux characteristics and stellar parameters. Then it is applied to the observed spectral data to calculate the atmospheric parameters. The main advantage of The Cannon is that it is not directly based on any stellar physical models, it has an even higher applicability. Moreover, because of the use of full-spectrum information, even for the spectra with a low signal-to-noise ratio (SNR), it still can obtain the parameter solutions of high reliability. This algorithm has significant advantages in the data processing and parameter determination of large-scale stellar spectra. In addition, this paper presents two examples of using The Cannon to obtain the stellar parameters of K and M giants from the LAMOST spectral data.     

Estimating stellar atmospheric parameters based on Lasso features

With the rapid development of large scale sky surveys like the Sloan Digital Sky Survey （SDSS）, GAIA and LAMOST （Guoshoujing telescope）, stellar spectra can be obtained on an ever-increasing scale. Therefore, it is necessary to estimate stel- lar atmospheric parameters such as Teff, log g and [Fe/H] automatically to achieve the scientific goals and make full use of the potential value of these observations. Feature selection plays a key role in the automatic measurement of atmospheric parameters. We propose to use the least absolute shrinkage selection operator （Lasso） algorithm to select features from stellar spectra. Feature selection can reduce redundancy in spectra, alleviate the influence of noise, improve calculation speed and enhance the robustness of the estimation system. Based on the extracted features, stellar atmospheric param- eters are estimated by the support vector regression model. Three typical schemes are evaluated on spectral data from both the ELODIE library and SDSS. Experimental results show the potential performance to a certain degree. In addition, results show that our method is stable when applied to different spectra.     

A non-parametric method of estimating the physical parameters of stellar atmosphere

The physical parameters of stellar atmosphere, e.g. the effective temperature, surface gravity and chemical abundance, are the main factors for the differences in stellar spectra, and the automatic measurement of these parameters is an important content in the automatic processing of the immense amount of spectral data provided by LAMOST and other patrol telescopes. Aiming at the estimation of the physical parameters for every star in large samples of stellar spectral data, a variable window-width algorithm is proposed in this article. It consists of the following three steps: (1) A PCA (principal component analysis) treatment of historical stellar spectral data is carried out to obtain a low-dimensional characteristic data of the spectra. (2) Establish the correlation between the characteristic data and the physical parameters using a non-parametric estimator with variable window-width. (3) By means of this estimator, the three physical parameters of the star are directly calculated. As shown by results of experiments, in comparison with the fixed window-width estimator and other algorithms reported in literature, our algorithm is more accurate and robust.     

Spectral Synthesis via Mean Field approach to Independent Component Analysis

We apply a new statistical analysis technique, the Mean Field approach to Independent Component Analysis(MF-ICA) in a Bayseian framework, to galaxy spectral analysis. This algorithm can compress a stellar spectral library into a few Independent Components(ICs), and the galaxy spectrum can be reconstructed by these ICs. Compared to other algorithms which decompose a galaxy spectrum into a combination of several simple stellar populations, the MF-ICA approach offers a large improvement in efficiency. To check the reliability of this spectral analysis method, three different methods are used:(1) parameter recovery for simulated galaxies,(2) comparison with parameters estimated by other methods, and(3) consistency test of parameters derived with galaxies from the Sloan Digital Sky Survey. We find that our MF-ICA method can not only fit the observed galaxy spectra efficiently, but can also accurately recover the physical parameters of galaxies. We also apply our spectral analysis method to the DEEP2 spectroscopic data, and find it can provide excellent fitting results for low signal-to-noise spectra.     

Parameterizing Stellar Spectra Using Deep Neural Networks

Large-scale sky surveys are observing massive amounts of stellar spectra.The large number of stellar spectra makes it necessary to automatically parameterize spectral data,which in turn helps in statistically exploring properties related to the atmospheric parameters.This work focuses on designing an automatic scheme to estimate effective temperature(T_(eff)),surface gravity(log g) and metallicity[Fe/H] from stellar spectra.A scheme based on three deep neural networks(DNNs) is proposed.This scheme consists of the following three procedures:first,the configuration of a DNN is initialized using a series of autoencoder neural networks;second,the DNN is fine-tuned using a gradient descent scheme;third,three atmospheric parameters T_(eff),log g and [Fe/H] are estimated using the computed DNNs.The constructed DNN is a neural network with six layers(one input layer,one output layer and four hidden layers),for which the number of nodes in the six layers are 3821,1000,500,100,30 and 1,respectively.This proposed scheme was tested on both real spectra and theoretical spectra from Kurucz's new opacity distribution function models.Test errors are measured with mean absolute errors(MAEs).The errors on real spectra from the Sloan Digital Sky Survey(SDSS) are 0.1477,0.0048 and 0.1129 dex for log g,log T_(eff) and [Fe/H](64.85 K for T_(eff)),respectively.Regarding theoretical spectra from Kurucz's new opacity distribution function models,the MAE of the test errors are 0.0182,0.0011 and 0.0112 dex for log g,log T_(eff) and [Fe/H](14.90 K for T_(eff)),respectively.     

Stellar Spectral Classification Based on Capsule Network

The rapid development of large-scale sky survey project has produced a large amount of stellar spectral data, which make the automatic classification of stellar spectral data a challenging task. In this paper, we have proposed a stellar spectral classification method based on a capsule network. At first, by using the one-dimensional convolutional network and short-time Fourier transform (STFT), the one-dimensional spectra of the F5, G5, and K5 types selected from the LAMOST Data Release 5 (DR5) are converted into the two-dimensional Fourier spectrum images. Then, the two-dimensional Fourier spectrum images are classified automatically by the capsule network. Because the capsule network can preserve the hierarchical pose relationships among the entities in the image, and it does not need any pooling layers, the experimental results show that the capsule network has a better classification performance, for the classifications of the F5, G5, and K5-type stellar spectra, its classification accuracy is superior to other classification methods.     

Recovering physical parameters from galaxy spectra using MOPED

We derive physical parameters of galaxies from their observed spectra using MOPED, the optimized data compression algorithm of Heavens, Jimenez & Lahav. Here we concentrate on parametrizing galaxy properties, and apply the method to the NGC galaxies in Kennicutt's spectral atlas. We focus on deriving the star formation history, metallicity and dust content of galaxies. The method is very fast, taking a few seconds of CPU time to estimate ∼17 parameters, and is therefore specially suited to studying large data sets, such as the Anglo-Australian two-degree-field (2dF) galaxy survey and the Sloan Digital Sky Survey (SDSS). Without the power of MOPED, the recovery of star formation histories in these surveys would be impractical. In Kennicutt's atlas, we find that for the spheroidals a small recent burst of star formation is required to provide the best fit to the spectrum. There is clearly a need for theoretical stellar atmospheric models with spectral resolution better than 1 Å if we are to extract all the rich information that large redshift surveys contain in their galaxy spectra.     

High-precision stellar abundances of the elements: methods and applications

Efficient spectrographs at large telescopes have made it possible to obtain high-resolution spectra of stars with high signal-to-noise ratio and advances in model atmosphere analyses have enabled estimates of high-precision differential abundances of the elements from these spectra, i.e. with errors in the range 0.01–0.03 dex for F, G, and K stars. Methods to determine such high-precision abundances together with precise values of effective temperatures and surface gravities from equivalent widths of spectral lines or by spectrum synthesis techniques are outlined, and effects on abundance determinations from using a 3D non-LTE analysis instead of a classical 1D LTE analysis are considered. The determination of high-precision stellar abundances of the elements has led to the discovery of unexpected phenomena and relations with important bearings on the astrophysics of galaxies, stars, and planets, i.e. (i) Existence of discrete stellar populations within each of the main Galactic components (disk, halo, and bulge) providing new constraints on models for the formation of the Milky Way. (ii) Differences in the relation between abundances and elemental condensation temperature for the Sun and solar twins suggesting dust-cleansing effects in proto-planetary disks and/or engulfment of planets by stars; (iii) Differences in chemical composition between binary star components and between members of open or globular clusters showing that star- and cluster-formation processes are more complicated than previously thought; (iv) Tight relations between some abundance ratios and age for solar-like stars providing new constraints on nucleosynthesis and Galactic chemical evolution models as well as the composition of terrestrial exoplanets. We conclude that if stellar abundances with precisions of 0.01–0.03 dex can be achieved in studies of more distant stars and stars on the giant and supergiant branches, many more interesting future applications, of great relevance to stellar and galaxy evolution, are probable. Hence, in planning abundance surveys, it is important to carefully balance the need for large samples of stars against the spectral resolution and signal-to-noise ratio needed to obtain high-precision abundances. Furthermore, it is an advantage to work differentially on stars with similar atmospheric parameters, because then a simple 1D LTE analysis of stellar spectra may be sufficient. However, when determining high-precision absolute abundances or differential abundance between stars having more widely different parameters, e.g. metal-poor stars compared to the Sun or giants to dwarfs, then 3D non-LTE effects must be taken into account.     

Empirical calibration of the near-infrared Ca ii triplet – II. The stellar atmospheric parameters

We present a homogeneous set of stellar atmospheric parameters ( T _eff, log  g , [Fe/H]) for a sample of about 700 field and cluster stars which constitute a new stellar library in the near-IR developed for stellar population synthesis in this spectral region ( λ 8350–9020) . Having compiled the available atmospheric data in the literature for field stars, we have found systematic deviations between the atmospheric parameters from different bibliographic references. The Soubiran, Katz & Cayrel sample of stars with very well determined fundamental parameters has been taken as our standard reference system, and other papers have been calibrated and bootstrapped against it. The obtained transformations are provided in this paper. Once most of the data sets were on the same system, final parameters were derived by performing error weighted means. Atmospheric parameters for cluster stars have also been revised and updated according to recent metallicity scales and colour–temperature relations.     

Statistical relations between stellar spectral and luminosity classes and stellar effective temperature and surface gravity

We have determined new statistical relations to estimate the fundamental atmospheric parameters of effective temperature and surface gravity, using MK spectral classification, and vice versa. The relations were constructed based on the published calibration tables(for main sequence stars) and observational data from stellar spectral atlases(for giants and supergiants). These new relations were applied to field giants with known atmospheric parameters, and the results of the comparison of our estimations with available spectral classification have been quite satisfactory.