Automatic Determination of Stellar Atmospheric Parameters Using Neural Networks and Instance-Based Machine Learning

In this article we show how machine learning methods can beeffectively applied to the problem of automatically predictingstellar atmospheric parameters from spectral information, a veryimportant problem in stellar astronomy. We apply feedforwardneural networks, Kohonen's self-organizing maps andlocally-weighted regression to predict the stellar atmosphericparameters effective temperature, surface gravity and metallicityfrom spectral indices. Our experimental results show that thethree methods are capable of predicting the parameters with verygood accuracy. Locally weighted regression gives slightly betterresults than the other methods using the original dataset asinput, while self-organizing maps outperform the other methods when significant amounts of noise are added. We also implemented a heterogeneous ensemble of predictors, combining the results given by the three algorithms. This ensemble yields better results than any of the three algorithms alone, using both the original and the noisy data.     

Accretion disk spectra of super-luminal jet sources and ultra-luminous compact X-ray sources in nearby spiral galaxies

The Ultra-luminous Compact X-ray Sources (ULXs)in nearby spiral galaxies and the Galactic super-luminaljet sources sharethe common spectral characteristic that they haveextremely high disk temperatures which cannot be explainedin the framework of the standard accretion disk modelin the Schwarzschild metric. We have calculated an extreme Kerr disk model to examine if the Kerr disk model can instead explain the observed `too hot' accretion disk spectra.We found that the Kerr disk spectrum becomes significantly hardercompared to the Schwarzschild disk only when the disk is highlyinclined.For super-luminal jet sources, which are known to beinclined systems, the Kerr disk model may thuswork if we choose proper values for the black hole angular momentum. For the ULXs, however, the Kerr disk interpretation will be problematic,as is is highly unlikely that their accretion disks are preferentiallyinclined.     

海量多波段星表数据的交叉证认的实现

许多天文学家需要星表来选源、证认和研究。目前国外有一些大的数据中心提供了星表查询、多源查询(即小样本交叉证认)、可视化等功能。但是随着大数据量及全波段天文时代的到来,这些服务已经越来越不能满足天文学家的需求。在详细分析了国外数据中心的服务后,提出了新的问题,并给出一套解决方案。着重讨论了任意两个星表的交叉证认和大数据量星表的交叉证认的实现,以及交叉证认结果的分类和参数的自由选择等方面的课题。     

Gyldén Model: The Slowly Changing Equivalent Gravitational Parameter

In the Gyldén problem, the case of slowly changing equivalent gravitational parameter (e.g.p.) is studied. Assuming the following law for the variation of the e.g.p.: μ (t) = μ₀+ εμ (εt), ε< μ₀, we obtained a) a O(ε⁴ T⁴)-approximation of the solution, on a shortened time scale (0,T), with T of order o(ε^-1),for the general case (i.e. the support function μ is O(1)-valued and admits an expansion in power series of its argument), and b) a O(ε²)-approximation of the solution, on a natural timescale of order O(1/ε), for the case of a bounded variation rate (i.e. the support function μ and its derivative are both O(1)-valued). For the study of the first problem we introduced a type of Lissajous variables and used the Lie-Hori normalization scheme; for the second problem we used the Delaunay variables and applied the von Zeipel method for approximate integration. The physical interpretation of the results is in both cases the same: within the corresponding limits of approximation, the variation of e.g.p. (i) has no effect on the size of the osculating ellipse, (ii) it sets the pericenter in slow rotation and (iii) it introduces a secular variation in the longitudes. This revised version was published online in July 2006 with corrections to the Cover Date.     

VO_IMPAT:中国虚拟天文台图像处理分析工具

中国虚拟天文台图像处理分析工具（VO_IMPAT），是由国家天文台虚拟天文台项目组开发的一项服务，它提供了对数字巡天图像数据（Digital Sky Survey，DSS）、天文星表以及其他数据库的交互访问。VO_IMPAT的设计目的是实现多波段天文数据的融合。可以将不同波段的星表叠加到DSS底图上，如光学波段的USNO星表、近红外波段的2MASS星表、射电波段的NVSS星表和X射线波段的RASS星表。同时VO_IMPAT还可以对图像进行放大、缩小、伪彩色、等高线、直方图、尖锐化、平滑化、旋转等处理，不同的星表数据可以采用不同的颜色和图标显示。     

Simulation of double cold cores of the 35°N section in the Yellow Sea with a wave-tide-circulation coupled model

Based on the MASNUM wave-tide-circulation coupled numerical model, the temperature structure along 35°N in the Yellow Sea was simulated and compared with the observations. One of the notable features of the temperature structure along 35°N section is the double cold cores phenomena during spring and summer. The double cold cores refer to the two cold water centers located near 122°E and 125°E from the depth of 30m to bottom. The formation, maintenance and disappearance of the double cold cores are discussed. At least two reasons make the temperature in the center (near 123°E) of the section higher than that near the west and east shores in winter. One reason is that the water there is deeper than the west and east sides so its heat content is higher. The other is invasion of the warm water brought by the Yellow Sea Warm Current (YSWC) during winter. This temperature pattern of the lower layer (from 30m to bottom) is maintained through spring and summer when the upper layer (0 to 30m) is heated and strong thermocline is formed. Large zonal span of the 35°N section (about 600 km) makes the cold cores have more opportunity to survive. The double cold cores phenomena disappears in early autumn when the west cold core vanishes first with the dropping of the thermocline position. Supported by the National Basic Research Program of China (No. G1999043809) and the National Science Foundation of China (No. 49736190).     

Discrimination of sedimentary facies by association analysis

The ability of association analysis to discriminate sedimentary facies was tested on Purdy's modal analyses of modern sediments of the Great Bahama Bank. Purdy's data set has served in the past as a standard reference for evaluating various multivariate classification algorithms. In order to adapt Purdy's data to association analysis, the percent abundance of the 12 constituents was converted to binary form by dichotomizing each variable on its mean value. The results obtained by association analysis are virtually identical to those obtained by Purdy and other authors. The same four main sedimentary facies were discriminated; 86% of the samples were identically classified (97% when misclassified borderline cases are counted as matches); the total partition variance of the classification is only negligibly greater (4%); and the grouping of the variables yielded the same four groups. The rank order of the three division-attributes responsible for the sample classification is fines, oolites, and corals. Association analysis has been employed by other authors to differentiate meaningful facies groups in studies of ancient reef carbonates, modern reef sediments, and heavy minerals in stream sediments. In all these studies, the results were found to be compatible with those obtained by using the continuous quantitative measurements, indicating that qualitative binary data may often be sufficient for the purpose of facies discrimination in many branches of geology and that association analysis is an effective method for this purpose.