共查询到17条相似文献，搜索用时 171 毫秒
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《天文学报》2016,(1)
郭守敬望远镜(Large Sky Area Multi-Object Fiber Spectroscopic Telescope,LAMOST)、斯隆数字巡天(Sloan Digital Sky Survey,SDSS)、英澳望远镜(AngloAustralia Telescope,AAT)等大多数多目标光纤光谱望远镜现用的数据处理流程都是基于一维算法的.以LAMOST为例提出多目标光纤光谱数据处理流程方法.在LAMOST现用数据处理流程中,在预处理过程之后,通过基于一维模型的抽谱算法从二维观测目标光谱数据中得到一维抽谱结果作为中间数据.后续的处理步骤都基于一维模型的算法.然而,这种数据处理流程不符合观测光谱的形成机理.因此,在每个步骤中都引入了不可忽略的误差.为了解决这一问题,提出了一种还未被用于LAMOST及其他望远镜数据处理系统的新颖的数据处理流程.重新设计安排了各个数据处理模块的顺序,各关键步骤算法都是基于二维模型的.核心算法将详细论述.此外,列出了部分实验结果来证明二维算法的有效性和优越性. 相似文献
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恒星形成区是研究恒星形成物理过程最重要的天体物理实验室. 猎户座分子云团是研究各种质量恒星形成和相关年轻恒星性质的一个著名天区. 通过对恒星形成区的光学光谱分析, 可以获取其内部热电离气体的运动学和化学性质. 基于国家大科学装置郭守敬望远镜(LAMOST)的光谱观测数据, 从LAMOST I期光谱巡天数据中筛选出8个指向猎户座分子云团的观测面板, 获取了1300多条针对猎户座分子云团内弥散电离介质的有效光谱. 选取不受星际介质污染的背景天光光谱构建超级天光, 对这些光谱数据做减天光处理, 并进一步测量其发射线性质，包括Ha、N Ⅱ] λ 6584、[S Ⅱ]λλ 6717和6731等发射线的中心波长和积分流量等.最后给出猎户座分子云团内弥散电离介质的视向速度和线强度比分布情况. 相似文献
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李凤云 韩智洁 刘明旭 淳于波 徐亚军 陈余 李双虎 闫秋浩 包铁威 郭王伟 李义成 李新南 王晋峰 田杰 乔撩云 陈亮 吕金虎 陈超 刘承 张勇 侯永辉 王跃飞 葛群 左恒 姜方华 李烨平 陆启帅 胡守伟 王佑 李爱华 倪季君 张超 倪小康 许静 李浩 李晓飞 张昊彤 白仲瑞 胡天柱 王丹 崔向群 赵永恒 李国平 《天文学报》2022,63(1):12-126
对大口径光学/红外天文望远镜而言,为保障其稳定高效运行,镜面镀膜是重要的维护环节之一.镀膜质量的好坏直接影响镜面光学反射率的高低,也严重影响到天文望远镜的成像质量、观测效率.国家重大科技基础设施—大天区面积多目标光纤光谱天文望远镜(Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST,又名郭守敬望远镜)自2009年6月通过国家竣工验收并逐步投入巡天观测,目前已进入第2个5 yr巡天计划阶段,取得近两千万的光谱产出和大批高显示度的科学成果. LAMOST共拥有24块施密特改正镜子镜和37块球面主镜子镜,为了确保在野外恶劣观测环境下镜面反射率维持在较高水平,每年要对大批子镜进行镀膜.主要介绍了镀膜需求、镀膜设备,并基于大量实验和多年的完善探索出一套可行的镀膜工艺流程,确保了LAMOST子镜极高的镀膜质量.镀膜后子镜平均反射率高达90%以上,满足了LAMOST光谱巡天的镜面反射率要求. 相似文献
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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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基于优先策略的动态选星算法 总被引：1，自引：0，他引：1
论述了LAMOST巡天观测战略系统(SSS)中观测策略的实现方法选星算法问题．以国外SDSS望远镜为例介绍了静态选星算法，分析了其不足之处，并结合LAMOST望远镜的特点，提出了一种新的选星算法-“动态选星”算法．动态选星算法基于优先策略原理，可以在满足覆盖完备性的基础上，优化观测效率，并能方便地兼顾观测条件的约束．给出了算法的原理和框架描述，并针对算法进行了模拟计算，证明了算法的有效性．另外需要指出的是动态选星算法不仅适用于LAMOST，它可以普遍地应用于多目标光纤望远镜的巡天选星． 相似文献
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在LAMOST和SDSS二维光谱数据处理的初始波长定标操作中,通常给定色散曲线的一组初始拟合系数,然后在其附近一定空间内进行搜索,以期找到一组最优的系数.这实际上是一个全局寻优的问题.LAMOST和SDSS目前使用的是枚举式搜索方式,但是由于缺乏全局最优解的先验知识,需要大量的时间遍历整个解空间才能得到全局最优解.遗传算法由于使用了启发式搜索方式,是一种高效的全局寻优算法.在标准遗传算法的框架上,通过使用有效的编码方式、适应度函数以及选择、交叉、变异等遗传算子,构造了一种能够用于初始波长定标的快速收敛的改进遗传算法.通过Shaffer's F6函数的测试,该改进遗传算法具有良好的全局收敛性.将该改进遗传算法引入到LAMOST初始波长定标的寻优操作中,实验表明该算法能够取得较好的效果. 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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Skylight is an important noise source in astronomical observations. The problem of sky-subtraction is an important factor that restricts the depth of multi-object fiber spectroscopic observations. The method of principal component analysis (PCA) comes from statistics, and it can be used to find the relations between the different skylight spectra and to obtain the skylight components contained in the object spectra. In order to study the sky-subtraction method of LAMOST, adopting a group of raw data from the Sloan Digital Survey System (SDSS), a simulation experiment is conducted, and the obtained result indicates that for the sky-subtraction, the PCA method is more effective than the SDSS reduction pipeline. In addition, a prospect is made for the application of the PCA method in LAMOST observations. 相似文献
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《天文和天体物理学研究(英文版)》2017,(9)
Sky subtraction is a key technique in data reduction of multi-fiber spectra. Knowledge of characteristics related to the instrument is necessary to determine the method adopted in sky subtraction.In this study, we describe the sky subtraction method designed for the Large sky Area Multi-Object fiber Spectroscopic Telescope(LAMOST) survey. The method has been integrated into the LAMOST 2D Pipeline v2.6 and applied to data from LAMOST DR3 and later. For LAMOST, calibration using sky emission lines is used to alleviate the position-dependent(and thus time-dependent) ~ 4% fiber throughput uncertainty and small wavelength instability(0.1 A) during observation. Sky subtraction using principal component analysis(PCA) further reduces 25% of the sky line residual from OH lines in the red part of LAMOST spectra after the master sky spectrum, which is derived from a B-spline fit of 20 sky fibers in each spectrograph. Using this approach, values are adjusted by a sky emission line and subtracted from each fiber. Further analysis shows that our wavelength calibration accuracy is about 4.5 km s~(-1), and the averages of residuals after sky subtraction are about 3% for sky emission lines and 3% for the continuum region. The relative sky subtraction residuals vary with moonlight background brightness, and can reach as low as 1.5% for regions that have sky emission lines during a dark night.Tests on F stars with both similar sky emission line strength and similar object continuum intensity show that the sky emission line residual of LAMOST is smaller than that of the SDSS survey. 相似文献
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Dong-Mei Qin Ping Guo Zhan-Yi Hu Yong-Heng ZhaoNational Laboratory of Pattern Recognition Laboratory Institute of Automation Chinese Academy of Sciences Beijing dmqin@nlpr.ia.ac.cnDepartment of Computer Sciences Beijing Normal University Beijing National Astronomical Observatories Chinese Academy of Sciences Beijing 《中国天文和天体物理学报》2003,3(3)
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
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《New Astronomy》2021
We use the random forest to regress the surface effective temperatures of stars in APOGEE from SDSS DR16 and LAMOST DR6. When the NUV-u, u-g, g-r, r-i, i-J, J-H, H-K, K-WISE_4_5 magnitudes are used as machine learning features, the coefficient of determination of regression are 94.91% in APOGEE and 90.46% in LAMOST. The standard deviation of the prediction and pipeline temperatures are 93.89K in APOGEE and 113.10K in LAMOST. When the NUV-J, J-H, H-K, K-WISE_4_5 magnitudes are used as features, the coefficient of determination of regression are 94.37% in APOGEE and 88.89% in LAMOST. The standard deviation is 96.59K in APOGEE and 119.92K in LAMOST. The J-H magnitudes are the most important feature to predict the effective temperatures, and the NUV-J magnitudes are the second important feature. The NUV-J, J-H, H-K, K-WISE_4_5 magnitudes are from the all-sky survey and can be employed widely to regress the effective temperatures of stars. 相似文献
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A novel method of lossless compression for astronomical spectra images is proposed in this paper. Firstly, Integer Wavelet Transform is adopted to perform decorrelation of the data. Afterwards, Embedded Zero-tree Wavelet encoder is employed to describe the zero-tree structure of wavelet coefficients, and then the resulting stream put through Embedded Zero-tree Wavelet encoder can be transformed to character string including only five characters that is easily compressed by entropy coding. Finally, Arithmetic encoder is chosen as the entropy coder here. Groups of simulation data based on LAMOST and observation data from SDSS are used in the experiment to demonstrate the new method, and the experimental results are much better than those of GZIP and JPEG2000. 相似文献
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With the rapid development of large scale sky surveys like the Sloan Digital Sky Survey （SDSS）, GAIA and LAMOST （Guoshoujing telescope）, stellar spectra can be obtained on an ever-increasing scale. Therefore, it is necessary to estimate stel- lar atmospheric parameters such as Teff, log g and [Fe/H] automatically to achieve the scientific goals and make full use of the potential value of these observations. Feature selection plays a key role in the automatic measurement of atmospheric parameters. We propose to use the least absolute shrinkage selection operator （Lasso） algorithm to select features from stellar spectra. Feature selection can reduce redundancy in spectra, alleviate the influence of noise, improve calculation speed and enhance the robustness of the estimation system. Based on the extracted features, stellar atmospheric param- eters are estimated by the support vector regression model. Three typical schemes are evaluated on spectral data from both the ELODIE library and SDSS. Experimental results show the potential performance to a certain degree. In addition, results show that our method is stable when applied to different spectra. 相似文献