共查询到19条相似文献，搜索用时 234 毫秒
1.
本提供了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
2.
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
3.
使用SOFM方法进行恒星光谱自动分类 总被引：1，自引：0，他引：1
ＳＯＦＭ是人工神经网络的非监督算法,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
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.
在对恒星形成区（ＳＦＲ）中ＲＯＳＡＴ选弱发射线ＴＴａｕｒｉ星（ＷＴＴＳ）进行光谱证认的过程中,发展了一套基于北京天文台２．１６米光学望远镜及其ＯＭＲ卡焦光谱仪系统的晚型星中色散（５０Ａ／ｍｍ）光谱计算机自动光谱分类方法．对ＲＯＳＡＴ选ＷＴＴＳ候选作中晚型星进行自动光谱分类的结果表明,一般情况下,光谱型的分类精度可达±１个次级,个别源为±２个次级．该自动光谱分类方法同时适用于其他光谱晚型星． 相似文献
6.
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
7.
利用星团谱样本的星族合成方法,分析了７个ＢＣＤＧ的光学谱,研究蓝致密矮星系（ＢＣＤＧ）内部的恒星形成历史、能源机制和尘埃分布情况．星族合成结果不仅能够解释ＢＣＤＧ的主要观测特征,解决ＢＣＤＧ研究中存在的一些问题,而且揭示了ＢＣＤＧ的一些未知的特性．谱合成结果表明：ＢＣＤＧ是年老的天体,其内部较早已经有恒星形成发生,恒星形成呈爆发式的,在ｔ＝５×１０７～１０９ｙｒ时的恒星形成率较高,最近（ｔ＝１０７ｙｒ）的恒星形成率开始下降;光谱分析显示大量大质量的年轻恒星提供了ＢＣＤＧ中心区的主要能源．利用扣除恒星吸收影响的发射线光谱,计算了发射线产生区的内红化值,发现它明显大于由连续谱计算得到的内红化值,这种差异的可能解释是ＢＣＤＧ内部尘埃对连续谱和发射线的产生区遮挡程度不同．最后,比较了ＢＣＧ和ＢＣＤＧ星族合成结果,提出了矮星系间一种可能的演化序列． 相似文献
8.
本文以恒星结构演化理论中常用的混合程对流理论为基础，给出了湍流压以及考虑湍流压情况下恒星内部物态方程和各热力学量的表达式．在此基础上研究了质量为２．８Ｍ⊙和７Ｍ⊙的恒星由主序演化到红巨星和ＡＧＢ星阶段其湍流压的大小．结果证明，在红巨星和ＡＧＢ阶段，靠近恒星表面区域内湍流压可以达到总压强的３０％． 相似文献
9.
低质量AGB星重元素的核合成 总被引：1，自引：0，他引：1
本文以（１３）Ｃ（α，ｎ）（１６）Ｏ作为中子源，考虑到恒星核心质量随热脉冲数的变化及星风、超星风质量损失的影响，采用从（５６）Ｆｅ到（２１０）Ｂｉ的无分支ｓ过程反应通道，拟合了ＭＳ、Ｓ的重元素超丰．本文特别将恒星质量与ＡＧＢ内Ｓ过程核合成模型结合起来讨论．结果表明质量较大的恒星因对流较强而稀释因子较大，ＭＳ、Ｓ星在轻、重Ｓ元素丰度关系图中分别落入四个不同区域，由此可以粗略估计这些恒星的质量．２．５Ｍ 的ＡＧＢ星形成具有环状空腔的星体，最后演化成Ｔｃ—ｎｏ单星，可以解释双星系统伴星为主序星的ＡＧＢ星无Ｔｃ现象． 相似文献
10.
晚型星计算机自动光谱分类 总被引：1，自引：1，他引：0
在对恒星形成区（ＳＦＲ）中ＲＯＳＡＴ选弱发射线Ｔ Ｔａｕｒｉ星（ＷＴＴＳ）进行光谱证认的过程中,发展了一套基于北京天台２．１６米光学望远镜及其ＯＭＲ卡焦光谱仪系统的晚型星中色散（５０Ａ／ｍｍ）光谱计算机自动光谱分类方法。对ＲＯＳＡＴ选ＷＴＴＳ候选体中晚型星进行自动光谱分类的结果表明,一般情况下,光谱型的分类精度可达±１个次级,个别源为±２个次级。该自动光谱分类方法同时适用于其它光谱晚型星。 相似文献
11.
Liu Zhong-bao 《Journal of Astrophysics and Astronomy》2016,37(2):12
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
12.
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
13.
《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
14.
《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
15.
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
16.
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
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.
Empirical effective temperatures of 211 early-type stars found in a previous investigation (Kontizas and Theodossiou, 1980; Theodossiou, 1985) are combined with the effective temperatures of other 313 early-type stars found from literature. From these effective temperatures of a total number of 524 early-type stars of spectral types from O8 to F6 we derive a new stellar temperature scale and the standard deviation of the MK spectral classification. 相似文献
19.
The close relation between the mass of the central black hole of galaxy and the stellar velocity dispersion of bulge indicates that it is of especial importance to accurately measure the stellar velocity dispersion for determining the mass of the central black hole of galaxy. A method which uses the spectra of SDSS (Sloan Digital Sky Survey) to measure the velocity dispersion and its uncertainty is provided in this paper. Through fitting four different spectral regions which contain remarkable characteristic absorption lines in pixel space, the spectral regions used to accurately measure the stellar velocity dispersion σ are obtained. In this paper, the absorption lines which are mainly contained in these four fitted bands are Ca II K, Mg I b triplet (with wavelengths of 5 167.5, 5 172.7, 5 183.6 Å) and CaT (Ca II triplet with wavelengths of 8 498.0, 8 542.1, 8 662.1 Å). As indicated by the results in different regions, the values of σ obtained by fitting the Mg I b region are small because this region is affected by the emission lines of iron group; the spectral line in the Ca II K line region is easily restricted to the searching algorithm of least square method because its strength is very weak; the stellar velocity dispersions obtained in the combined region of CaT and Ca II K are equivalent to the results given by calculating only the CaT region. This method is used to test a sample of Seyfert galaxies whose redshifts are less than 0.05. It is found that the CaT region is the best spectral region for measuring the stellar velocity dispersion. 相似文献