共查询到20条相似文献，搜索用时 31 毫秒
1.
Stellar Spectral Classification using Principal Component Analysis and Artificial Neural Networks 总被引：1，自引：0，他引：1
Harinder P.Singh Ravi K.Gulati RanjanGupta 《Monthly notices of the Royal Astronomical Society》1998,295(2):312-318
A fast and robust method of classifying a library of optical stellar spectra for O to M type stars is presented. The method employs, as tools: (1) principal component analysis (PCA) for reducing the dimensionality of the data and (2) multilayer back propagation network (MBPN) based artificial neural network (ANN) scheme to automate the process of classification. We are able to reduce the dimensionality of the original spectral data to very few components by using PCA and are able to successfully reconstruct the original spectra. A number of NN architectures are used to classify the library of test spectra. Performance of ANN with this reduced dimension shows that the library can be classified to accuracies similar to those achieved by Gulati et al. but with less computational load. Furthermore, the data compression is so efficient that the NN scheme successfully classifies to the desired accuracy for a wide range of architectures. The procedure will greatly improve our capabilities in handling and analysing large spectral data bases of the future. 相似文献
2.
《New Astronomy》2021
With the increase of stellar spectra, how to automatically classify these spectra have attracted astronomer's attention. Support Vector Machine (SVM), as a typical classifier, has widely used in stellar spectra classification. Due to its limited performance in various classification problems and higher training time, a model with a pair of hyperspheres named Twin Hypersphere Model (THM), proposed by Peng and Xu, is utilized for stellar spectra classification in this paper. In THM, the samples in one hypersphere is far from another according to the Euclidean distance. The comparative experiments with SVM and Twin Support Vector Machine (TWSVM) on the SDSS datasets shows that the THM model gives the best classification accuracy of 0.8836 for type F, 0.9446 for type G, and 0.9509 for type K, which are better than the classification accuracies of 0.8000, 0.8484, 0.8911 obtained by SVM and 0.8413, 0.8699, 0.9109 obtained by TWSVM. It can be concluded that THM perform better than traditional techniques such as SVM and TWSVM on the K-, F-, G- type stellar spectra classification. 相似文献
4.
《天文和天体物理学研究(英文版)》2015,(8)
In this work, we select spectra of stars with high signal-to-noise ratio from LAMOST data and map their MK classes to the spectral features. The equivalent widths of prominent spectral lines, which play a similar role as multi-color photometry, form a clean stellar locus well ordered by MK classes. The advantage of the stellar locus in line indices is that it gives a natural and continuous classification of stars consistent with either broadly used MK classes or stellar astrophysical parameters. We also employ an SVM-based classification algorithm to assign MK classes to LAMOST stellar spectra. We find that the completenesses of the classifications are up to 90% for A and G type stars, but they are down to about 50% for OB and K type stars. About 40% of the OB and K type stars are mis-classified as A and G type stars,respectively. This is likely due to the difference in the spectral features between late B type and early A type stars or between late G and early K type stars being very weak. The relatively poor performance of the automatic MK classification with SVM suggests that the direct use of line indices to classify stars is likely a more preferable choice. 相似文献
5.
Zhong-bao Liu Fang-xiao Zhou Zhen-tao Qin Xue-gang Luo Jing Zhang 《Astrophysics and Space Science》2018,363(7):140
Support Vector Machine (SVM) is a popular data mining technique, and it has been widely applied in astronomical tasks, especially in stellar spectra classification. Since SVM doesn’t take the data distribution into consideration, and therefore, its classification efficiencies can’t be greatly improved. Meanwhile, SVM ignores the internal information of the training dataset, such as the within-class structure and between-class structure. In view of this, we propose a new classification algorithm-SVM based on Within-Class Scatter and Between-Class Scatter (WBS-SVM) in this paper. WBS-SVM tries to find an optimal hyperplane to separate two classes. The difference is that it incorporates minimum within-class scatter and maximum between-class scatter in Linear Discriminant Analysis (LDA) into SVM. These two scatters represent the distributions of the training dataset, and the optimization of WBS-SVM ensures the samples in the same class are as close as possible and the samples in different classes are as far as possible. Experiments on the K-, F-, G-type stellar spectra from Sloan Digital Sky Survey (SDSS), Data Release 8 show that our proposed WBS-SVM can greatly improve the classification accuracies. 相似文献
6.
Liu Zhong-Bao 《Journal of Astrophysics and Astronomy》2016,37(2):9
Support Vector Machine (SVM) is one of the important stellar spectral classification methods, and it is widely used in practice. But its classification efficiencies cannot be greatly improved because it does not take the class distribution into consideration. In view of this, a modified SVM named Minimum within-class and Maximum between-class scatter Support Vector Machine (MMSVM) is constructed to deal with the above problem. MMSVM merges the advantages of Fisher’s Discriminant Analysis (FDA) and SVM, and the comparative experiments on the Sloan Digital Sky Survey (SDSS) show that MMSVM performs better than SVM. 相似文献
7.
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. 相似文献
8.
大型巡天项目的快速发展,产生大量的恒星光谱数据,也使得实现恒星光谱数据的自动分类成为一项具有挑战性的工作.提出一种新的基于胶囊网络的恒星光谱分类方法,首先利用1维卷积网络和短时傅里叶变换将来源于LAMOST(Large Sky Area Multi-Object Fiber Spectroscopy Telescope)Data Release 5(DR5)的F5、G5、K5型1维恒星光谱转化成2维傅里叶谱图像,再通过胶囊网络对2维谱图像进行自动分类.由于胶囊网络具有保留图像中实体之间的分层位姿关系和无需池化层的优点,实验结果表明:胶囊网络具有较好的分类性能,对于F5、G5、K5型恒星光谱的分类,准确率优于其他分类方法. 相似文献
9.
Noriyuki Matsunaga 《Astrophysics and Space Science》2012,337(1):93-98
We present an automatic, fast, accurate and robust method of classifying astronomical objects. The Self Organizing Map (SOM)
as an unsupervised Artificial Neural Network (ANN) algorithm is used for classification of stellar spectra of stars. The SOM
is used to make clusters of different spectral classes of Jacoby, Hunter and Christian (JHC) library. This ANN technique needs
no training examples and the stellar spectral data sets are directly fed to the network for the classification. The JHC library
contains 161 spectra out of which, 158 spectra are selected for the classification. These 158 spectra are input vectors to
the network and mapped into a two dimensional output grid. The input vectors close to each other are mapped into the same
or neighboring neurons in the output space. So, the similar objects are making clusters in the output map and making it easy
to analyze high dimensional data. 相似文献
10.
With the availability of multi-object spectrometers and the design and operation of some large scale sky surveys, the issue of how to deal with enormous quantities of spectral data efficiently and accurately is becoming more and more important. This work investigates the classification problem of stellar spectra under the assumption that there is no perfect absolute flux calibration, for example, when considering spectra from the Guo Shou Jing Telescope(the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST). The proposed scheme consists of the following two procedures: Firstly, a spectrum is normalized based on a 17 th order polynomial fitting; secondly, a random forest(RF) is utilized to classify the stellar spectra. Experiments on four stellar spectral libraries show that the RF has good classification performance. This work also studied the spectral feature evaluation problem based on RF. The evaluation is helpful in understanding the results of the proposed stellar classification scheme and exploring its potential improvements in the future. 相似文献
11.
《Chinese Astronomy and Astrophysics》2006,30(2):176-186
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. 相似文献
12.
《Chinese Astronomy and Astrophysics》2021,45(3):352-363
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. 相似文献
13.
本文提供了１２５颗ＭＫ标准星的ＣＣＤ光谱,光谱型从Ｏ到Ｍ,光度级从Ｖ到Ⅰ,构成较完整的二元分类框架,光谱覆盖范围由传统蓝紫区延伸到黄红区．初步考察和归纳了黄红区适于恒星分类的主要光谱特征和判据．这些结果对于采用相似分辨率的恒星光谱分类工作是非常有用的． 相似文献
14.
恒星大气物理参量的非参数估计方法 总被引：1，自引：0，他引：1
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． 相似文献
15.
G. S. D. Babu 《Journal of Astrophysics and Astronomy》1983,4(4):235-252
Low-dispersion spectra of the order of 1000 Å mm-1 have been obtained for stars in several faint galactic clusters with a transmission grating placed in front of the photographic plate at the Cassegrain focus of the Kavalur 102-cm telescope. The intensity distribution in the shorter wavelengths has been taken as the principal criterion for the spectral classification of the individual stars in the area covered by the photographic plate. The uncertainty in this procedure has been found to be about two spectral subclasses. A combination of these spectral classes with the visual magnitudes derived from the image diameters on the POSS charts provide the HR diagrams for each cluster area. These diagrams are adequate to establish the cluster membership of any star to a first approximation. This technique has been tested on six galactic open clusters, four of which are well-studied. We find good agreement both in terms of the ages of the clusters and individual stellar membership. 相似文献
16.
天体光谱分类是天文学研究的重要内容之一,其关键是从光谱数据中选择和提取对分类识别最有效的特征构建特征空间.提出一种新的基于2维傅里叶谱图像的特征提取方法,并应用于LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope)恒星光谱数据的分类研究中.光谱数据来源于LAMOST Data Release 5(DR5),选取30000条F、 G和K型星光谱数据,利用短时傅里叶变换(Short-Time Fourier Transform, STFT)将1维光谱数据变换成2维傅里叶谱图像,对得到的2维傅里叶谱图像采用深度卷积网络模型进行分类,得到的分类准确率是92.90%.实验结果表明通过对LAMOST恒星光谱数据进行STFT可得到光谱的2维傅里叶谱图像,谱图像构成了新的光谱数据特征和特征空间,新的特征对于光谱数据分类是有效的.此方法是对光谱分类的一种全新尝试,对海量天体光谱的分类和挖掘处理有一定的开创意义. 相似文献
17.
With the use of modern detectors stellar spectral classification libraries have been extended from the photographic regime to the near ℝ at 11000 Å. We have defined new spectral indices within this extended wavelength-range that can be used to determine the luminosity classification for G-K-M stars. An advantage of the new indices, which sample the stellar flux in and out of selected spectral features, is that they are insensitive to catalog differences. This facilitates the use of many catalogs, with varying resolution, different reddening corrections, and calibrations, hence extending the total number of stellar standards available. Furthermore, we demonstrate that the indices can be used to infer absolute magnitudes with good accuracy. The indices should prove useful for analysis of spectra from distant clusters, galaxies, and in particular for problems involving spectral synthesis of stellar populations of galaxies. 相似文献
18.
19.
Giuseppina Micela 《Experimental Astronomy》2015,40(2-3):723-732
Stellar activity is the major astrophysical limiting factor for the study of planetary atmospheres. Its variability and spectral characteristics may affect the extraction of the planetary signal even for moderately active stars. A technique based on spectral change in the visible band was developed to estimate the effects in the infrared due to star activity. This method has been purposely developed for the EChO mission which had the crucial characteristics of monitoring simultaneously a broadband from visible to infrared. Thanks to this capability the optical spectrum, whose variations are mainly due to stellar activity, has been used as in an instantaneous calibrator to correct the infrared spectrum. The technique is based on principal component analysis which significantly reduces the dimensionality of the spectra. The method was tested on a set of simulations with realistic photon noise. It can be generalized to any chromatic variability effects provided that optical and infrared variations are correlated. 相似文献
20.
ＳＯＦＭ是人工神经网络的非监督学习算法,可以将数据组织到一个特征图上,而保存 大多数原始数据空间的拓扑特征．使用这种方法进行恒星光谱自动分类,分类结果与哈佛 序列十分相似．ＳＯＦＭ方法应该是进行大数量恒星光谱样本在线分类的有用方法,它能 够自动执行,因此可用于处理大数量天体光谱． 相似文献