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1.
《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
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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
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An automated spectral classification technique for large sky surveys is pro-posed. We firstly perform spectral line matching to determine redshift candidates for an observed spectrum, and then estimate the spectral class by measuring the similarity be-tween the observed spectrum and the shifted templates for each redshift candidate. As a byproduct of this approach, the spectral redshift can also be obtained with high accuracy. Compared with some approaches based on computerized learning methods in the liter-ature, the proposed approach needs no training, which is time-consuming and sensitive to selection of the training set. Both simulated data and observed spectra are used to test the approach; the results show that the proposed method is efficient, and it can achieve a correct classification rate as high as 92.9%, 97.9% and 98.8% for stars, galaxies and quasars, respectively. 相似文献
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定义了一个新的量,曲率宽度,去检查同步模型与伽玛射线暴(GRB)光谱的一致性.此量用于测量GRB中辐射能谱(νFν,ν和Fν分别是频率和随频率变化的能量流量)峰值处的光谱拐折锐度.然后使用它检查了理论同步模型与观测到的GRB光谱之间的一致性.首先计算几种典型的同步模型的曲率宽度,包括单能、单幂律和拐折幂律电子同步模型.其次从Fermi/GBM (Gamma-ray Burst Monitor)长GRB时间分辨光谱目录中选择包含1198个光谱的GRB样本,将光谱与常用的经验模型拟合,并计算最佳拟合模型的光谱曲率宽度.通过比较两个曲率宽度,发现大多数样本与同步模型不一致,因为同步模型的光谱拐折比数据的光谱拐折更加平滑.结果表明同步模型很难适合大多数观测到的GRB光谱.此外,在暴脉冲中发现光子流量和曲率宽度之间存在强的反相关性,这表明流量越高,光谱拐折越尖锐,或者与同步模型的偏差就越大. 相似文献
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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
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PLS (Partial Least Squares regression) is introduced into an automatic esti-mation of fundamental stellar spectral parameters. It extracts the most correlative spec-tral component to the parameters (Teff, log g and [Fe/H]), and sets up a linear regres-sion function from spectra to the corresponding parameters. Considering the properties of stellar spectra and the PLS algorithm, we present a piecewise PLS regression method for estimation of stellar parameters, which is composed of one PLS model for Teff, and seven PLS models for log g and [Fe/H] estimation. Its performance is investigated by large experiments on flux calibrated spectra and continuum normalized spectra at dif-ferent signal-to-noise ratios (SNRs) and resolutions. The results show that the piecewise PLS method is robust for spectra at the medium resolution of 0.23 nm. For low resolu-tion 0.5 nm and 1 nm spectra, it achieves competitive results at higher SNR. Experiments using ELODIE spectra of 0.23 nm resolution illustrate that our piecewise PLS models trained with MILES spectra are efficient for O ～ G stars: for flux calibrated spectra, the systematic offsets are 3.8%, 0.14dex, and -0.09 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.44 dex and 0.38 dex, respectively; for continuum normalized spectra, the systematic offsets are 3.8%, 0.12dex, and -0.13 dex for Teff, log g and [Fe/H], with error scatters of 5.2%, 0.49 dex and 0.41 dex, respectively. The PLS method is rapid, easy to use and does not rely as strongly on the tightness of a parameter grid of templates to reach high precision as Artificial Neural Networks or minimum distance methods do. 相似文献
8.
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
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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
10.
光谱预处理及其对星系/类星体分类结果的影响 总被引：1，自引：0，他引：1
由于噪声、畸变和观测环境等因素的影响,在天体光谱自动处理之前,需要对它进行相应的预处理.研究巡天光谱的预处理(数据格式和流量标准化)对光谱自动分析的影响.分析了同数据格式对光谱及其谱线特征的影响和格式标准化研究的必要性;通过分析光谱流量数量级的不确定性及其特点,提出了流量数量级变化的基本模型,并给出了相应的标准化方法.通过星系和类星体的分类实验,结果表明:1)采用对数波长数据格式对光谱的自动分类更有利;2)验证了所提出的流量标准化模型的合理性,以及所给流量标准化方法良好的性能.特别需要指出的是,文献中通常采用的流量标准化方法在光谱自动分类中的效果反而是较差的. 相似文献
11.
巡天观测与高能物理、黑洞天文等领域均有密切的联系.基于星系-超新星二分类问题,研究光谱数据预处理,结合余弦相似度改善PCA(Principal Component Analysis)光谱分解特征提取方法,用SDSS(the Sloan Digital Sky Survey)、WISeREP(the Weizmann Interactive Supernova data REPository)组成的5620条光谱数据集训练支持向量机,可以得到0.498%泛化误差的识别模型和新样本分类概率.使用Neyman-Pearson决策方法建立NPSVM(Neyman-Pearson Support Vector Machine)模型可进一步降低超新星的漏判率. 相似文献
12.
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
13.
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
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本文给出十颗Me星的低色散光谱资料,其中有六颗星以前没有人给出发射线资料,四颗前人没有给出光谱资料;还有一颗是新发现的Me星。这里我们均给出发射线资料及光谱型。另外还给出了每颗星的光谱描迹图及其中三颗星的证认图。 相似文献
16.
Mei-fang Zhao Chao Wu A-li Luo Fu-chao Wu Yong-heng Zhao 《Chinese Astronomy and Astrophysics》2007,31(4):352-362
By means of a batch of low-redshift spectral data of AGNs taken from the SDSS, an automated K-nearest neighbor method is developed to classify AGNs into two types: broad-line and narrow-line AGNs. According to the different characteristics of emission lines of broad-line and narrow-line AGNs, the spectral wavebands containing the Hβ, [OIII], H and [NII] emission lines are used separately or in combination in the classification. experiment. The results show that the best results are obtained when only the wavebands of H and [NII] are used, and that for a training set of size 1000 and a testing set of 3313, we can achieve a speed of 32.89 single classifications per second. It is demonstrated that, where the typical spectral features are sufficiently exploited, the automated classification method is feasible for the spectra of AGNs in largescale spectral surveys and provides a fast and straightforward alternative to classification schemes based on using the FWHM values of emission lines or the line strength ratio diagnostic diagrams. 相似文献
17.
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
18.
R. G. Griffiths R. S. Warwick I. Georgantopoulos C. Done & D. A. Smith 《Monthly notices of the Royal Astronomical Society》1998,298(4):1159-1168
We use non-simultaneous Ginga ASCA ROSAT observations to investigate the complex X-ray spectrum of the Seyfert 2 galaxy Mrk 3. We find that the composite spectrum can be well described in terms of a heavily cut-off hard X-ray continuum, iron Kα emission and a soft X-ray excess, with spectral variability confined to changes in the continuum normalization and the flux in the iron line. Previous studies have suggested that the power-law continuum in Mrk 3 is unusually hard. We obtain a canonical value for the energy index of the continuum (i.e., α ≈ 0.7) when a warm absorber (responsible for an absorption edge observed near 8 keV) is included in the spectral model. Alternatively, the inclusion of a reflection component yields a comparable power-law index. The soft-excess flux cannot be modelled solely in terms of pure electron scattering of the underlying power-law continuum. However, a better fit to the spectral data is obtained if we include the effects of both emission and absorption in a partially photoionized scattering medium. In particular, the spectral feature prominent at ∼ 0.9 keV could represent O VIII recombination radiation produced in a hot photoionized medium. We discuss our results in the context of other recent studies of the soft X-ray spectra of Seyfert 2 galaxies. 相似文献
19.
星系的形态与星系的形成和演化息息相关, 其形态学分类是星系天文学后续研究的重要一环. 当前海量天文观测数据的出现使得天文数据自动分析方法越来越得到重视, 针对此问题, 利用先进的深度学习骨干网络EfficientNetV2, 分析不同的注意力机制类型和使用节点对网络性能的影响, 构建了一种命名为EfficientNetV2-S-Triplet7 (即在EfficientNetV2-S stage7的$1\times1$卷积层后加入Triplet模块)的改进算法模型来实现星系形态学的自动分类. 使用第二期星系动物园(Galaxy Zoo 2, GZ2)中超过24万张的测光图像作为初始数据进行实验测试. 在对数据进行预处理时采取了尺寸抖动、翻转、色彩畸变等图像增强手段来解决图像数量的不平衡问题. 在同一系列经典和前沿的深度学习算法模型AlexNet、ResNet-34、MobileNetV2、RegNet进行对比实验后, 得出EfficientNetV2-S-Triplet7算法在分类准确率、查全率和F1分数等指标上具有最好的测试结果. 在9375张测试图像中的3项指标值分别可达到89.03%、90.21%、89.93%, 查准率达到89.69%, 在其他模型中排在第3位. 该结果表明将EfficientNetV2-S-Triplet7算法应用于大规模星系数据的形态学分类任务中有很好的效果. 相似文献
20.
F. C. Riddick P. F. Roche P. W. Lucas 《Monthly notices of the Royal Astronomical Society》2007,381(3):1067-1076
The spectral type is a key parameter in calibrating the temperature which is required to estimate the mass of young stars and brown dwarfs. We describe an approach developed to classify low-mass stars and brown dwarfs in the Trapezium Cluster using red optical spectra, which can be applied to other star-forming regions. The classification uses two methods for greater accuracy: the use of narrow-band spectral indices which rely on the variation of the strength of molecular lines with spectral type and a comparison with other previously classified young, low-mass objects in the Chamaeleon I star-forming region. We have investigated and compared many different molecular indices and have identified a small number of indices which work well for classifying M-type objects in nebular regions. The indices are calibrated for young, pre-main-sequence objects whose spectra are affected by their lower surface gravities compared with those on the main sequence. Spectral types obtained are essentially independent of both reddening and nebular emission lines.
Confirmation of candidate young stars and brown dwarfs as bona fide cluster members may be accomplished with moderate resolution spectra in the optical region by an analysis of the strength of the gravity-sensitive Na doublet. It has been established that this feature is much weaker in these very young objects than in field dwarfs. A sodium spectral index is used to estimate the surface gravity and to demonstrate quantitatively the difference between young (1–2 Myr) objects, and dwarf and giant field stars. 相似文献
Confirmation of candidate young stars and brown dwarfs as bona fide cluster members may be accomplished with moderate resolution spectra in the optical region by an analysis of the strength of the gravity-sensitive Na doublet. It has been established that this feature is much weaker in these very young objects than in field dwarfs. A sodium spectral index is used to estimate the surface gravity and to demonstrate quantitatively the difference between young (1–2 Myr) objects, and dwarf and giant field stars. 相似文献