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1.
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.  相似文献   

2.
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   

3.
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   

4.
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   

5.
With the help of computer tools and algorithms, automatic stellar spectral classification has become an area of current interest. The process of stellar spectral classification mainly includes two steps: dimension reduction and classification. As a popular dimensionality reduction technique, Principal Component Analysis (PCA) is widely used in stellar spectra classification. Another dimensionality reduction technique, Locality Preserving Projections (LPP) has not been widely used in astronomy. The advantage of LPP is that it can preserve the local structure of the data after dimensionality reduction. In view of this, we investigate how to apply LPP+SVM in classifying the stellar spectral subclasses. In the comparative experiment, the performance of LPP is compared with PCA. The stellar spectral classification process is composed of the following steps. Firstly, PCA and LPP are respectively applied to reduce the dimension of spectra data. Then, Support Vector Machine (SVM) is used to classify the 4 subclasses of K-type and 3 subclasses of F-type spectra from Sloan Digital Sky Survey (SDSS). Lastly, the performance of LPP+SVM is compared with that of PCA+SVM in stellar spectral classification, and we found that LPP does better than PCA.  相似文献   

6.
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   

7.
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   

8.
新一代大规模光谱巡天项目产生了近千万条低分辨率恒星光谱,基于这些光谱数据,介绍一种名为The Cannon的机器学习方法。该方法完全基于已知恒星大气参数(有效温度、表面重力加速度和金属丰度等)的光谱数据,通过数据驱动来构建特征向量,建立光谱流量特征和恒星参数的函数对应关系,进而应用到观测光谱数据中,实现对恒星光谱的大气参数求解。The Cannon的主要优势为不直接基于任何恒星物理模型,适用性更广;由于使用了全谱信息,即便对于低信噪比光谱也能得到较高可信度的参数结果,该算法在大规模恒星光谱的数据处理和参数求解方面具有明显的优势。此外,还利用The Cannon得到LAMOST光谱数据中K巨星和M巨星的恒星参数。  相似文献   

9.
For LAMOST, the largest sky survey program in China, the solution of the problem of automatic discrimination of stars from galaxies by spectra has shown that the results of the PSF test can be significantly refined. However, the problem is made worse when the redshifts of galaxies are not available. We present a new automatic method of star/(normal) galaxy separation, which is based on Statistical Mixture Modeling with Radial Basis Function Neural Networks (SMM-RBFNN). This work is a continuation of our previous one, where active and non-active celestial objects were successfully segregated. By combining the method in this paper and the previous one, stars can now be effectively separated from galaxies and AGNs by their spectra-a major goal of LAMOST, and an indispensable step in any automatic spectrum classification system. In our work, the training set includes standard stellar spectra from Jacoby's spectrum library and simulated galaxy spectra of EO, SO, Sa, Sb types with redshift ranging from 0 to 1  相似文献   

10.
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   

11.
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.  相似文献   

12.
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   

13.
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   

14.
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   

15.
A method for the determination of [α/Fe] from low-resolution stellar spectra is presented. The proposed scheme includes the following three steps: firstly, the spectrum is decomposed by the multi-scale Haar wavelet, and the high-frequency components are removed to suppress the high-frequency noise; then, based on the correlation of the spectral data component with [α/Fe], the spectral features are selected by the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm; finally, [α/Fe] is measured by the multiple linear regression method based on the MARCS stellar spectrum library. The effectiveness of the method is verified with the low-resolution stellar spectra of ELODIE, SDSS (Sloan Digital Sky Survey), LAMOST (Large Sky Area Multi-Object Fibre Spectroscopic Telescope), and four star clusters. The systematic deviations and accuracies are as follows: (0.04 dex, 0.064 dex) for the 317 ELODIE spectra; (0.16 dex, 0.065 dex) for the 412 SDSS spectra; (0.05 dex, 0.062 dex) for the 1276 LAMOST spectra (with the signal-noise ratio in the g band (SNRG) greater than 20). The averages of [α/Fe] obtained for the likely members of the globular star clusters (M13, M15) and open star clusters (NGC2420, M67) are in agreement with the literature values.  相似文献   

16.
In this paper we present an application of an artificial neural network model based on a multi-layered backpropagation algorithm for spectral classification of UV data from the International Ultraviolet Explorer (IUE) low dispersion spectra reference atlas. The model used is similar to that of von Hippel et al. (1994), and is found to reduce the classification error as compared to the recently reported results on the same data set (Gulati et al. 1994b). The improved version of the network is much simpler in structure and the training time is reduced by a factor of almost 20. Such networks will prove very useful in efficient classification of large databases Subject headings: neural networks, stellar spectra, classification  相似文献   

17.
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   

18.
New high-quality high spectral resolution observations of the HI line emission from massive young stellar objects are described and discussed. It is proposed that two distinct physical components contribute to the observed emission. One of these is an optically-thick high-velocity stellar wind, the other a more slowly moving optically-thin volume of gas that may, in the case of S106IR at least, be caused by mass loading of the stellar wind. This decomposition is shown to resolve a long-standing problem regarding the relative widths of high and low opacity lines.  相似文献   

19.
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   

20.
In this paper we present a method that combines evolution strategies (ES) and standard optimization algorithms to solve the problem of fitting line profiles of stellar spectra. This method provides a reliable decomposition and a reduction in computing time over conventional algorithms. Using a stellar spectrum as input, we implemented an evolution strategy to find an approximation of the continuum spectrum and spectral lines. After a few generations, the parameters found by ES are given as starting search point to a standard optimization algorithm, which then finds the correct spectral decomposition. We used Gaussian functions to fit spectral lines and the Planck function to represent the continuum spectrum. Our experimental results present the application of this method to real spectra, showing that they can be approximated very accurately. This revised version was published online in July 2006 with corrections to the Cover Date.  相似文献   

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