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71.
The paper deals with the method based on treating the axial meridian circle and a long-focus light mark in the prime vertical as a single virtual instrument that has properties of meridian circle. A horizontal axis, a sighting line, and a micrometer cross-wire of the instrument are created by optical means. The key function here is performed by the light mark that ensures a reference direction used for formation of instrumental principal planes and directions. Stability of instrumental azimuth, inclination, and collimation is expected to be about ±0.″01. Besides, an accuracy of observations is not affected by any temperature and weight deformations of telescope mechanics. Owing to the last circumstance, the design is cheap and the telescope aperture may be increased significantly, to about 1m. An estimate of accuracy of observations depends on the adopted model of image motion, and for sites with excellent seeing and bright stars is ±0.″03 to ±0.″05 (rms). 相似文献
72.
接收机是射电天文中用于探测微弱射电信号的重要接收设备.接收机的强度校准就是将接收机对射电源的响应转换为天文意义上的流量密度.常规方法就是使用经典的冷热负载法,将接收机自身的强度响应转换为一个等效的温度值,之后再据此对射电源做进一步标定.通过搭建基于斩波轮技术的K波段接收机强度校准平台,使用斩波轮法测试K波段常温接收机的噪声温度,并与传统冷热负载法的测试结果进行比对.结果显示,在晴好天气条件下,斩波轮法在30°、90°仰角下噪声温度的最大测试误差为7.5%和8.4%,可以很好地应用于实际噪声温度测试中;但在5°仰角测试中,由于过低仰角引入了地面噪声,使得斩波轮法的测试误差上升至20%–30%之间而无法使用.希望在此基础上进一步开展K波段天空亮温度的理论计算与实测,从而完善斩波轮技术的应用,使之可以满足在不同气象条件下的噪声校准测试需求. 相似文献
73.
机器学习在当今诸多领域已经取得了巨大的成功,但是机器学习的预测效果往往依赖于具体问题.集成学习通过综合多个基分类器来预测结果,因此,其适应各种场景的能力较强,分类准确率较高.基于斯隆数字巡天(Sloan Digital Sky Survey,SDSS)计划恒星/星系中最暗源星等集分类正确率低的问题,提出一种基于Stacking集成学习的恒星/星系分类算法.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星等集、暗源星等集和最暗源星等集.仅针对分类较为复杂且困难的最暗源星等集展开分类研究.首先,对最暗源星等集使用10折嵌套交叉验证,然后使用支持向量机(Support Vector Machine,SVM)、随机森林(Random Forest,RF)、XGBoost(eXtreme Gradient Boosting)等算法建立基分类器模型;使用梯度提升树(Gradient Boosting Decision Tree,GBDT)作为元分类器模型.最后,使用基于星系的分类正确率等指标,与功能树(Function Tree,FT)、SVM、RF、GBDT、XGBoost、堆叠降噪自编码(Stacked Denoising AutoEncoders,SDAE)、深度置信网络(Deep Belief Network,DBN)、深度感知决策树(Deep Perception Decision Tree,DPDT)等模型进行分类结果对比分析.实验结果表明,Stacking集成学习模型在最暗源星等集分类中要比FT算法的星系分类正确率提高了将近10%.同其他传统的机器学习算法、较强的提升算法、深度学习算法相比,Stacking集成学习模型也有较大的提升. 相似文献
74.
《Chinese Astronomy and Astrophysics》2020,44(1):41-60
AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset. 相似文献
75.
电子地图一边吸收传统地图学理论的精髓,同时引进新的技术和方法,摆脱了传统地图制图的局限和束缚,成为一种地理学研究与应用的新的语言形式。通过对传统地图同电子地图产品的比较来阐述地图产品在数字技术背景下产品模式、制图技术及应用领域等方面的变革。 相似文献
76.
天体搜索是天文数据处理流程的一个重要环节,也是以平方公里阵列射电望远镜(Square Kilometre Array, SKA)为代表的下一代射电望远镜在面向海量数据处理中的挑战之一.现今天体自动搜索算法、软件已日趋成熟并投入应用,不过在自动化、兼容性等方面仍具有提升空间.以更自动化、更适应海量数据需求的天体搜索算法研究为宗旨,以现有算法为研究基础,天体自动搜索软件系统得到设计和开发.该系统包含友好的交互式用户操作界面,具备可视化输出数据显示、兼容不同数据输入和输出并包含为实际应用服务的文件管理功能.该系统对于大天区图以及图像集,均能够很好地进行自动化处理.测试结果显示,上述方法对于天体搜索的改进有一定成效.后续将在此基础上对该集成系统做进一步的改进开发,以适应更多的需求. 相似文献
77.
《Chinese Astronomy and Astrophysics》2019,43(3):424-443
The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility. 相似文献
78.
《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
79.
A.R.Mermut 《山地科学学报》2009,6(2):107-112
The book "micropedolog" by Kubieana and a large number of publications has induced many people to practice soil micromorphology. Quantification of the soil fabric and its components was a major challenge. The use of the image analyses in soil science was a breakthrough. Attempts to make soil thin sections go back to the beginning of the 2oth century. Microscopic techniques and recently high resolution electron microscope and use of computer assisted imaging techniques enabled the in vitro study of soils in three dimensional levels. It is now possible to store and process massive amounts of data. Micro- morphological concepts and techniques are applied in paleopedological, ecological, and archaeological studies. The aim of this work was to examine soil micromorphological imaging in historical perspective. 相似文献
80.
James R. Ledwell Dennis J. McGillicuddy Jr. Laurence A. Anderson 《Deep Sea Research Part II: Topical Studies in Oceanography》2008,55(10-13):1139
An intense deep chlorophyll layer in the Sargasso Sea was reported near the center of an anticyclonic mode-water eddy by McGillicuddy et al. [2007. Eddy–wind interactions stimulate extraordinary mid-ocean plankton blooms, Science, accepted]. The high chlorophyll was associated with anomalously high concentrations of diatoms and with a maximum in the vertical profile of 14C primary productivity. Here we report tracer measurements of the vertical advection and turbulent diffusion of deep-water nutrients into this chlorophyll layer. Tracer released in the chlorophyll layer revealed upward motion relative to isopycnal surfaces of about 0.4 m/d, due to solar heating and mixing. The density surfaces themselves shoaled by about 0.1 m/d. The upward flux of dissolved inorganic nitrogen, averaged over 36 days, was approximately 0.6 mmol/m2/d due to both upwelling and mixing. This flux is about 40% of the basin wide, annually averaged, nitrogen flux required to drive the annual new production in the Sargasso Sea, estimated from the oxygen cycle in the euphotic zone, the oxygen demand below the euphotic zone, and from the 3He excess in the mixed layer. The observed upwelling of the fluid was consistent with theoretical models [Dewar, W.K., Flierl, G.R., 1987. Some effects of wind on rings. Journal of Physical Oceanography 17, 1653–1667; Martin, A.P., Richards, K.J., 2001. Mechanisms for vertical nutrient transport within a North Atlantic mesoscale eddy. Deep-Sea Research II 48, 757–773] in which eddy surface currents cause spatial variations in surface stress. The diapycnal diffusivity at the base of the euphotic zone was 3.5±0.5×10−5 m2/s. Diapycnal mixing was probably enhanced over more typical values by the series of storms passing over the eddy during the experiment and may have been enhanced further by the trapping of near-inertial waves generated within the eddy. 相似文献