Stellar Spectral Classification with Minimum Within-Class and Maximum Between-Class Scatter Support Vector Machine

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.     

Classification of Stellar Spectra with Fuzzy Minimum Within-Class Support Vector Machine

Classification is one of the important tasks in astronomy, especially in spectra analysis. Support Vector Machine (SVM) is a typical classification method, which is widely used in spectra classification. Although it performs well in practice, its classification accuracies can not be greatly improved because of two limitations. One is it does not take the distribution of the classes into consideration. The other is it is sensitive to noise. In order to solve the above problems, inspired by the maximization of the Fisher’s Discriminant Analysis (FDA) and the SVM separability constraints, fuzzy minimum within-class support vector machine (FMWSVM) is proposed in this paper. In FMWSVM, the distribution of the classes is reflected by the within-class scatter in FDA and the fuzzy membership function is introduced to decrease the influence of the noise. The comparative experiments with SVM on the SDSS datasets verify the effectiveness of the proposed classifier FMWSVM.     

Stellar spectra classification with twin hypersphere model

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.     

Stellar Spectral Classification with Locality Preserving Projections and Support Vector Machine

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.     

Spectral classification of stars based on LAMOST spectra

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.     

A discussion on metallicity effects on the period-luminosity relations of Cepheids and Miras

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.     

The small scatter in BH–host correlations and the case for self-regulated BH growth

Supermassive black holes (BHs) obey tight scaling relations between their mass and host galaxy properties such as total stellar mass, velocity dispersion and potential well depth. This has led to the development of self-regulated models for BH growth, in which feedback from the central BH halts its own growth upon reaching a critical threshold. However, models have also been proposed in which feedback plays no role: so long as a fixed fraction of the host gas supply is accreted, relations like those observed can be reproduced. Here, we argue that the scatter in the observed BH–host correlations presents a demanding constraint on any model for these correlations, and that it favours self-regulated models of BH growth. We show that the scatter in the stellar mass fraction within a radius R in observed ellipticals and spheroids increases strongly at small R . At a fixed total stellar mass (or host velocity dispersion), on very small scales near the BH radius of influence, there is an order-of-magnitude scatter in the amount of gas that must have entered and formed stars. In short, the BH appears to 'know more' about the global host galaxy potential on large scales than the stars and gas supply on small scales. This is predicted in self-regulated models; however, models where there is no feedback would generically predict order-of-magnitude scatter in the BH–host correlations. Likewise, models in which the BH feedback in the 'bright' mode does not regulate the growth of the BH itself, but sets the stellar mass of the galaxy by inducing star formation or blowing out a mass in gas much larger than the galaxy stellar mass, are difficult to reconcile with the scatter on small scales.     

MK标准星的CCD光谱及光谱分类判据向黄红区的扩展

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．     

A multi-layered backpropagation artificial neural network algorithm for UV spectral classification

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     

使用SOFM方法进行恒星光谱自动分类

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．     

Synthetic stellar and SSP libraries as templates for Gaia simulations

ESA’s Gaia mission will collect low resolution spectroscopy in the optical range for ～10⁹ objects. Complete and up-to-date libraries of synthetic stellar spectra are needed to built algorithms aimed to automatically derive the classification and the parametrization of this huge amount of data. In addition, libraries of stellar spectra are one of the main ingredients of stellar population synthesis models, aiming to derive the properties of unresolved stellar populations from their integrated light. We present (a) the newly computed libraries of synthetic spectra built by the Gaia community, covering the whole optical range (300–1100 nm) at medium-high resolution of (0.3 nm) for stars spanning the most different types, from M to O, from A-peculiar to Emission lines to White Dwarfs, and (b) the implementation of those libraries in our SSP code (Tantalo in The Initial Mass Function 50 Years Later, 327:235 2005), exploring different stellar evolution models.     

Asymmetries of Thomson scatter spectra from the terrestrial ionosphere

Asymmetries of Thomson scatter spectra occurring at different heights have been investigated through differential ion-flow technique using BGK form of Boltzmann equation. The result is useful to study the frequency broadening and bite-out centre of power spectra of incoherent scatter signals.     

Using Artificial Intelligence techniques to classify IRAS LRS spectra of evolved stars

The IRAS Low Resolution Spectrometer (LRS) covered the spectral region from 7µm to 23µm, and an Atlas was produced containing 5425 spectra. Most of the spectra were associated with evolved stars, including over 3000 spectra from the dust shells around O-rich stars. When Artificial Intelligence techniques were applied to the dataset, a new classification was derived. A scheme with 77 classes, grouped into 9 metaclasses, resulted, and for those types of spectra which were well represented in the initial dataset (i.e. the evolved stars) a very subtle classification was derived, often using line shapes, relative line strengths, or the presence of additional weak features.     

Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

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     

Dust-penetrated arm classes: insights from rising and falling rotation curves

In the last decade, near-infrared imaging has highlighted the decoupling of gaseous and old stellar discs: the morphologies of optical (Population I) tracers compared to the old stellar disc morphology, can be radically different. Galaxies which appear multi-armed and even flocculent in the optical may show significant grand-design spirals in the near-infrared. Furthermore, the optically determined Hubble classification scheme does not provide a sound way of classifying dust-penetrated stellar discs: spiral arm pitch angles (when measured in the near-infrared) do not correlate with Hubble type. The dust-penetrated classification scheme of Block & Puerari provides an alternative classification based on near-infrared morphology, which is thus more closely linked to the dominant stellar mass component. Here we present near-infrared K -band images of 14 galaxies, on which we have performed a Fourier analysis of the spiral structure in order to determine their near-infrared pitch angles and dust-penetrated arm classes. We have also used the rotation curve data of Mathewson et al. to calculate the rates of shear in the stellar discs of these galaxies. We find a correlation between near-infrared pitch angle and rate of shear: galaxies with wide open arms (the γ class) are found to have rising rotation curves, while those with falling rotation curves belong to the tightly wound α bin. The major determinant of near-infrared spiral arm pitch angle is the distribution of matter within the galaxy concerned. The correlation reported in this study provides the physical basis underpinning spiral arm classes in the dust-penetrated regime and underscores earlier spectroscopic findings by Burstein and Rubin that Hubble type and mass distributions are unrelated.     

基于胶囊网络的恒星光谱分类研究

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.     

Stellar spectral classification and feature evaluation based on a random forest

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.     

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.     

一种新的基于2维傅里叶谱图像的恒星光谱特征提取方法和深度网络分类应用

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.