共查询到19条相似文献，搜索用时 343 毫秒
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机器学习在当今的诸多领域已经取得了巨大的成功.尤其是提升算法.提升算法适应各种场景的能力较强、准确率较高,已经在多个领域发挥巨大的作用.但是提升算法在天文学中的应用却极为少见.为解决斯隆数字巡天(Sloan Digital Sky Survey,SDSS)数据中恒星/星系暗源集分类正确率低的问题,引入了机器学习中较新的研究成果–XGBoost (eXtreme Gradient Boosting).从SDSS-DR7 (SDSS Data Release 7)中获取完整的测光数据集,并根据星等值划分为亮源集和暗源集.首先,分别对亮源集和暗源集使用十折交叉验证法,同时运用XGBoost算法建立恒星/星系分类模型;然后,运用栅格搜索等方法调优XGBoost参数;最后,基于星系的分类正确率等指标,与功能树(Function Tree, FT)、Adaboost (Adaptive boosting)、随机森林(Random Forest, RF)、梯度提升决策树(Gradient Boosting Decision Tree, GBDT)、堆叠降噪自编码(Stacked Denoising AutoEncoders, SDAE)、深度置信网络(Deep Belief Network, DBN)等模型进行对比并分析结果.实验结果表明:XGBoost在暗源分类中要比功能树算法的星系分类正确率提高了将近10%,在暗源集的最暗星等中比功能树提高了将近5%.同其他传统的机器学习算法和深度神经网络相比, XGBoost也有不同程度的提升. 相似文献
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低表面亮度星系(Low Surface Brightness Galaxy, LSBG)的特征对于理解星系整体特征非常重要, 通过现代的机器学习特别是深度学习算法来搜寻扩充低表面亮度星系样本具有重要意义. LSBG因特征不明显而难以用传统方法进行自动和准确辨别, 但深度学习确具有自动找出复杂且有效特征的优势, 针对此问题提出了一种可用于在大样本巡天观测项目中搜寻LSBG的算法---YOLOX-CS (You Only Look Once version X-CS). 首先通过实验对比5种经典目标检测算法并选择较优的YOLOX算法作为基础算法, 然后结合不同注意力机制和不同优化器, 构建了YOLOX-CS的框架结构. 数据集使用的是斯隆数字化巡天(Sloan Digital Sky Survey, SDSS)中的图像, 其标签来自于$\alpha.40$-SDSS DR7 (40%中性氢苜蓿巡天与第7次数据发布的斯隆数字化巡天的交叉覆盖天区)巡天项目中的LSBG, 由于该数据集样本较少, 还采用了深度卷积生成对抗网络(Deep Convolutional Generative Adversarial Networks, DCGAN)模型扩充了实验测试数据. 通过与一系列目标检测算法对比后, YOLOX-CS在扩充前后两个数据集中搜索LSBG的召回率和AP (Average Precision)值都有较好的测试结果, 其在未扩充数据集的测试集中的召回率达到97.75%, AP值达到97.83%, 在DCGAN模型扩充的数据集中, 同样测试集下进行实验的召回率达到99.10%, AP值达到98.94%, 验证了该算法在LSBG搜索中具有优秀的性能. 最后, 将该算法应用到SDSS部分测光数据上, 搜寻得到了765个LSBG候选体. 相似文献
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从Sloan数字巡天第2批释放的数据(SDSS DR2)中选择了395个在r波段亮于15等的面向晚型旋涡星系作为样本,研究了盘的颜色梯度与结构参数的关系.结果表明:盘的颜色梯度与盘的绝对星等(质量)无关;盘的颜色梯度与盘的尺度有关,越大的盘颜色梯度越陡;盘的颜色梯度与盘的颜色有关,越蓝的盘颜色梯度越陡;盘的颜色梯度与盘的表面亮度有关,越亮的盘颜色梯度越陡,并简单讨论了盘的颜色梯度与各结构参数的相关关系对晚型旋涡星系盘恒星形成历史的约束. 相似文献
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基于半解析模型和SDSS DR4(Sloan Digital Sky Survey Data Release 4),研究了环境对星系性质的影响.通过对以颜色、恒星形成率和恒星质量的带权重的相关函数的测量,发现半解析模型在颜色和恒星质量上与SDSS数据符合得比较好,而恒星形成率则在SDSS数据中表现为与环境无关.此结论证实了半解析模型对星系中部分性质与环境关系的预测,但对于恒星形成率为何与环境无关仍有待研究. 相似文献
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随着天文探测技术的快速发展, 海量的星系图像数据不断产生, 能够及时高效地对星系图像进行形态分类对研究星系的形成与演化至关重要. 针对传统的星系形态分类模型特征选择困难、分类速度慢、准确率受限等难题, 提出一种以Inception-v3神经网络为主干结构, 融合压缩激励(Squeeze and Excitation Network, SE)通道注意力机制的星系形态分类模型. 该模型在斯隆数字巡天(Sloan Digital Sky Survey, SDSS)样本的测试集准确率高达99.37%. 旋涡星系、圆形星系、中间星系、雪茄状星系与侧向星系的F1值分别为99.33%、99.58%、99.33%、99.41%与99.16%. 该模型与Inception-v3、MobileNet (Mobile Neural Network)和ResNet (Residual Neural Network)网络模型相比, SE-Inception-v3宽度和深度优势表现出更强的特征提取能力, 可以高效识别不同形态的星系, 为未来大型巡天计划的大规模星系形态分类问题提供了一种新方法. 相似文献
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基于COSMOS(Cosmic Evolution Survey)/Ultra VISTA(Ultra-deep Visible and Infrared Survey Telescope for Astronomy)场中多波段测光数据,利用质量限选取了红移分布在0z3.5的星系样本.通过UVJ(U-V和V-J)双色图分类判据将星系分类成恒星形成星系(SFGs)和宁静星系(QGs).对于红移分布在0z1.5范围内且M*1011M⊙的QGs来说,该星系在样本中所占比例高于70%.在红移0z3.5范围内,恒星形成星系的恒星形成率(SFR)与恒星质量(M*)之间有着很强的主序(MS)关系.对于某一固定的恒星质量M*来说,星系的SFR和比恒星形成率(s SFR)会随着红移增大而增大,这表明在高红移处恒星形成星系更加活跃,有激烈的恒星形成.相对于低质量的星系来说,高质量的SFGs有较低的s SFR,这意味着低质量星系的增长更多的是通过星系本身的恒星形成.通过结合来自文献中数据点信息,发现更高红移(2z8)星系的s SFR随红移的演化趋势变弱,其演化关系是s SFR∝(1+z)0.94±0.17. 相似文献
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基于NASA/IPAC河外星系数据库(NASA/IPAC Extragalactic Database,NED)和Sloan数字巡天(Sloan Digital Sky Survey,SDSS)第8次释放的数据(The Eighth Data Release,DR8),对星系团Abell 85(以下简称A85)的2倍动力学特征半径2r_(200)内的光度函数(Luminosity Function,LF)进行了研究.研究表明,A85的光度函数在Sloan巡天5个波段用Schechter函数均能拟合得很好.在u、g和z波段光度函数都显示出1个下凹.早型星系r波段的两个最佳拟合参数(r波段特征绝对星等和暗端的陡度)分别为M_r~*=-21.14_(-0.17)~(+0.17)mag,α=-0.83_(-0.14)~(+0.12),晚型星系为M_r~*=-21.98_(-0.98)~(+0.84)mag,α=-1.5_(-0.35)~(+0.24).早型星系的特征星等暗于晚型星系,而暗端比晚型星系要平坦得多.早型星系的光度函数在-20.5~-20.0 mag下凹.将1.5r_(200)范围内的星系按距离团中心的远近划分为3个环状区域,发现距离团中心越近,光度函数的暗端越陡,特征星等越亮. 相似文献
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从COMBO-17数字巡天数据里,选择了CDFS(Chandra Deep Field South)天区中1231个测光红移在0.1～0.3之间的暗蓝星系作为样本,研究了这些星系分别在只有光学波段和光学加近红外波段数据情况下做测光红移得到的红移分布,以及这些星系在静止参考系下的能谱分布(Spectral Energy Distributions,SEDs)特征.结果表明有183个星系在利用光学加近红外波段数据做测光红移时得到的红移大于1.2,它们的误差为0.046,提高测光的信噪比也有利于区分这类被光学波段误认为低红移的星系.这些暗蓝星系中高红移星系的观测近红外流量相对于光学流量有上升的趋势,而低红移星系的观测近红外流量相对于光学流量有下降的趋势. 相似文献
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利用从斯隆数字巡天(Sloan Digital Sky Survey,简称SDSS)第4次释放的光谱数据中选取的10~5个发射线星系样本,研究了[O_Ⅱ]λ3727/Hα流量比与星系尘埃消光、气体电离态和金属丰度的关系.发现尘埃消光改正对[O_Ⅱ]λ3727/Hα谱线流量比影响显著,消光改正前、后的[O_Ⅱ]λ3727/Hα谱线流量比的中值分别为0.48和0.89;尘埃消光改正后,F([O_Ⅱ]λ3727)-F(Hα)的弥散显著减小.贫金属星系的[O_Ⅱ]λ3727/Hα谱线流量比随星系气体的电离度增高而减小,而富金属星系不存在这种关系.另外,[O_Ⅱ]λ3727/Hα流量比与星系金属丰度相关.当12+lg(O/H)8.5时,星系[O_Ⅱ]λ3727/Hα流量比随金属丰度增加而下降;12+lg(O/H)8.5的星系,谱线流量比与金属丰度正相关.最后,利用气体电离度参数和星系的金属丰度,给出了计算不同类型星系[O_Ⅱ]λ3727/Hα流量比的公式.LAMOST望远镜将观测到大量红移z0.4的星系光谱,利用该公式可以给出星系的[O_Ⅱ]λ3727/Hα流量比,从而可以利用[O_Ⅱ]λ3727谱线流量计算z0.4星系的恒星形成率. 相似文献
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《Chinese Astronomy and Astrophysics》2020,44(3):345-355
Machine learning has achieved great success in many areas today, but the forecast effect of machine learning often depends on the specific problem. An ensemble learning forecasts results by combining multiple base classifiers. Therefore, its ability to adapt to various scenarios is strong, and the classification accuracy is high. In response to the low classification accuracy of the darkest source magnitude set of stars/galaxies in the Sloan Digital Sky Survey (SDSS), a star/galaxy classification algorithm based on the stacking ensemble learning is proposed in this paper. The complete photometric data set is obtained from the SDSS Data Release (DR) 7, and divided into the bright source magnitude set, dark source magnitude set, and darkest source magnitude set according to the stellar magnitude. Firstly, the 10-fold nested cross-validation method is used for the darkest source magnitude set, then the Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms are used to establish the base-classifier model; the Gradient Boosting Decision Tree (GBDT) is used as the meta-classifier model. Finally, based on the classification accuracy of galaxies and other indicators, the classification results are analyzed and compared with the results obtained by the Function Tree (FT), SVM, RF, GBDT, Stacked Denoising Autoencoders (SDAE), Deep Belief Nets (DBN), and Deep Perception Decision Tree (DPDT) models. The experimental results show that the stacking ensemble learning model has improved the classification accuracy of galaxies in the darkest source magnitude set by nearly 10% compared to the function tree algorithm. Compared with other traditional machine learning algorithm, stronger lifting algorithm, and deep learning algorithm, the stacking ensemble learning model also has different degrees of improvement. 相似文献
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《Chinese Astronomy and Astrophysics》2019,43(4):539-548
Machine learning has achieved great success in many areas today. The lifting algorithm has a strong ability to adapt to various scenarios with a high accuracy, and has played a great role in many fields. But in astronomy, the application of lifting algorithms is still rare. In response to the low classification accuracy of the dark star/galaxy source set in the Sloan Digital Sky Survey (SDSS), a new research result of machine learning, eXtreme Gradient Boosting (XGBoost), has been introduced. The complete photometric data set is obtained from the SDSS-DR7, and divided into a bright source set and a dark source set according to the star magnitude. Firstly, the ten-fold cross-validation method is used for the bright source set and the dark source set respectively, and the XGBoost algorithm is used to establish the star/galaxy classification model. Then, the grid search and other methods are used to adjust the XGBoost parameters. Finally, based on the galaxy classification accuracy and other indicators, the classification results are analyzed, by comparing with the models of function tree (FT), Adaptive boosting (Adaboost), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Stacked Denoising AutoEncoders (SDAE), and Deep Belief Nets (DBN). The experimental results show that, the XGBoost improves the classification accuracy of galaxies in the dark source classification by nearly 10% as compared to the function tree algorithm, and improves the classification accuracy of sources with the darkest magnitudes in the dark source set by nearly 5% as compared to the function tree algorithm. Compared with other traditional machine learning algorithms and deep neural networks, the XGBoost also has different degrees of improvement. 相似文献
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M. M. Lanyon-Foster C. J. Conselice M. R. Merrifield 《Monthly notices of the Royal Astronomical Society》2007,380(2):571-584
We present a study of pixel colour–magnitude diagrams (pCMDs) for a sample of 69 nearby galaxies chosen to span a wide range of Hubble types. Our goal is to determine how useful a pixel approach is for studying galaxies according to their stellar light distributions and content. The galaxy images were analysed on a pixel-by-pixel basis to reveal the structure of the individual pCMDs. We find that the average surface brightness (or projected mass density) in each pixel varies according to galaxy type. Early-type galaxies exhibit a clear 'prime sequence' and some pCMDs of face-on spirals reveal 'inverse-L' structures. We find that the colour dispersion at a given magnitude is found to be approximately constant in early-type galaxies but this quantity varies in the mid and late types. We investigate individual galaxies and find that the pCMDs can be used to pick out morphological features. We discuss the discovery of 'Red Hooks' in the pCMDs of six early-type galaxies and two spirals and postulate their origins. We develop quantitative methods to characterize the pCMDs, including measures of the blue-to-red light ratio and colour distributions of each galaxy and we organize these by morphological type. We compare the colours of the pixels in each galaxy with the stellar population models of Bruzual & Charlot to calculate star formation histories for each galaxy type and compare these to the stellar mass within each pixel. Maps of pixel stellar mass and mass-to-light ratio are compared to galaxy images. We apply the pCMD technique to three galaxies in the Hubble Ultra Deep Field to test the usefulness of the analysis at high redshift. We propose that these results can be used as part of a new system of automated classification of galaxies that can be applied at high redshift. 相似文献
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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
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星系的光谱包含其内部恒星的年龄和金属丰度等信息, 从观测光谱数据中测量这些信息对于深入了解星系的形成和演化至关重要. LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Telescope)巡天发布了大量的星系光谱, 这些高维光谱与它们的物理参数之间存在着高度的非线性关系. 而深度学习适合于处理多维、海量的非线性数据, 因此基于深度学习技术构建了一个8个卷积层$+$4个池化层$+$1个全连接层的卷积神经网络, 对LAMOST Data Release 7 (DR7)星系的年龄和金属丰度进行自动估计. 实验结果表明, 使用卷积神经网络通过星系光谱预测的星族参数与传统方法基本一致, 误差在0.18dex以内, 并且随着光谱信噪比的增大, 预测误差越来越小. 实验还对比了卷积神经网络与随机森林回归模型、深度神经网络的参数测量结果, 结果表明卷积神经网络的结果优于其他两种回归模型. 相似文献
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S. Kaviraj J. E. G. Devriendt I. Ferreras S. K. Yi 《Monthly notices of the Royal Astronomical Society》2005,360(1):60-68
The colour–magnitude relation (CMR) of cluster elliptical galaxies has been widely used to constrain their star formation histories (SFHs) and to discriminate between the monolithic collapse and merger paradigms of elliptical galaxy formation. We use a Λ cold dark matter hierarchical merger model of galaxy formation to investigate the existence and redshift evolution of the elliptical galaxy CMR in the merger paradigm. We show that the SFH of cluster ellipticals predicted by the model is quasi-monolithic , with only ∼10 per cent of the total stellar mass forming after z ∼ 1 . The quasi-monolithic SFH results in a predicted CMR that agrees well with its observed counterpart in the redshift range 0 < z < 1.27 . We use our analysis to argue that the elliptical-only CMR can be used to constrain the SFHs of present-day cluster ellipticals only if we believe a priori in the monolithic collapse model. It is not a meaningful tool for constraining the SFH in the merger paradigm, since a progressively larger fraction of the progenitor set of present-day cluster ellipticals is contained in late-type star-forming systems at higher redshift, which cannot be ignored when deriving the SFHs. Hence, the elliptical-only CMR is not a useful discriminant between the two competing theories of elliptical galaxy evolution. 相似文献
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Alan F. Heavens Raul Jimenez Ofer Lahav 《Monthly notices of the Royal Astronomical Society》2000,317(4):965-972
We present a method for radical linear compression of data sets where the data are dependent on some number M of parameters. We show that, if the noise in the data is independent of the parameters, we can form M linear combinations of the data which contain as much information about all the parameters as the entire data set, in the sense that the Fisher information matrices are identical; i.e. the method is lossless. We explore how these compressed numbers fare when the noise is dependent on the parameters, and show that the method, though not precisely lossless, increases errors by a very modest factor. The method is general, but we illustrate it with a problem for which it is well-suited: galaxy spectra, the data for which typically consist of ∼103 fluxes, and the properties of which are set by a handful of parameters such as age, and a parametrized star formation history. The spectra are reduced to a small number of data, which are connected to the physical processes entering the problem. This data compression offers the possibility of a large increase in the speed of determining physical parameters. This is an important consideration as data sets of galaxy spectra reach 106 in size, and the complexity of model spectra increases. In addition to this practical advantage, the compressed data may offer a classification scheme for galaxy spectra which is based rather directly on physical processes. 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