Automated Separation of Stars and Normal Galaxies Based on Statistical Mixture Modeling with RBF Neural Networks

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     

A Faraday rotation template for the Galactic sky

Using a set of compilations of measurements for extragalactic radio sources, we construct all-sky maps of the Faraday rotation produced by the Galactic magnetic field. In order to generate the maps, we treat the radio source positions as a kind of 'mask' and construct combinations of spherical harmonic modes that are orthogonal on the masked sky. As long as relatively small multipoles are used, the resulting maps are quite stable to changes in the selection criteria for the sources, and show clearly the structure of the local Galactic magnetic field. We also suggest the use of these maps as templates for cosmic microwave background (CMB) foreground analysis, illustrating the idea with a cross-correlation analysis between the Wilkinson Microwave Anisotropy Probe ( WMAP ) data and our maps. We find a significant cross-correlation, indicating the presence of a significant residual contamination.     

REVIEWS OF GEOGRAPHIC SOFTWARE

Reviews of geographic software in this article: DEMO-GRAPHICS: WORLD POPULATIONS AND PROJECTIONS. ESP GAUSS. CEMODEL S. Damus LIMDEP. William H. Greene MICROSTAT 4.1 OTIS PCIPS. (Personal Computer Image Processing System) . H.J. Meyers and R. Bernstein. REGRESSION ANALYSIS OF TIME SERIES (RATS) SPSS/PC+ URBAN DATA MANAGEMENT SOFTWARE (UDMS)     

临猗5.0级地震前后品质因子值变化特征研究

利用西安数字地震遥测台网记录的数字地震资料，采用P波初动半周期残差法求得1998年7月临猗5．0级地震前后不同路径的Q(品质因子)值变化，发现在地震发生前Q值为87～203，震后Q值为67～164，震前震中区附近出现明显的高Q值异常。结果表明，地震前的高Q值异常可以作为地震预测的一种手段。     

球状星团的天体测量(英)

球状星团是银河系中最年老的天体之一，是储存着银河系早期演化珍贵信息的“化石”．球状星团的天体测量，主要包括球状星因天区内恒星相对自行的测定，并由这些相对自行数据采用适当方法定出星团的绝对自行，或者直接测定绝对自行．利用这些自行数据，或者进一步与测光和视向速度数据结合，可以开展与球状星团的距离、运动、动力学状况、质量、年龄、演化等等以及银河系的结构和演化等有关的一系列重要的研究．在本文中对本世纪70年代中期以来在球状星团相对自行测定和成员概率估计、内部运动检测、绝对自行测定和空间运动研究这三方面取得的成果和进展以及存在的问题作了评述．     

Implications of low-pass filtering on power spectra and autocorrelation functions of turbulent velocity signals

Filtering either through the electronics of an instrument or through digital procedure is performed routinely on geophysical data. When velocity fluctuations are measured in turbulent flows using electromagnetic current meters (ECMs), a builtin lowpass Butterworth filter of order n usually attenuates fluctuations at high frequencies. However, the effects of this filter may not be acknowledged in turbulence studies, thus impeding comparisons between data collected with different ECMs. This paper explores the implications of the filters on the characteristics of velocity signals, mainly on variance, power spectra, and correlation analyses. Variance losses resulting from filtering can be important but will vary with the order n of the Butterworth filter, decreasing as n increases. Knowing the filter response, it is possible to reconstruct the original signal spectrum to evaluate the effect of filtering on variance and to allow comparisons between data collected with different instruments. The autocorrelation function also is affected by filtering which increases the value of the coefficients in the first lags, resulting in an overestimation of the integral length scale of coherent structures. These important effects add to those related to size and shape differences in ECM sensors and must be taken into account in comparative studies.