我国近年来天文的新发现

这里叙述的新发现仅指某一特殊类型的发现（如新星，超新星和小行星等）及某一特殊类型天体的认证（如X射线源证认为类星体等），而不包括某一类天体的某种天象及未知性质源的列表（如深度成像观测得到的前人没有纪录过的一般天体，如恒星，但一些特殊天体亦列入），由于收集不全面和所谓“发现”定义的困难，向有发向而未列入者先歉意。从80年代后期开始，我国的观测设备有了很大垢发展，所以有一批重的的天文发现，为天文提供了很好的机会，它包括下面几方面，层系层次天体：（1）用X射线源和射电源证认了一批包括类星体，BL Lac天体，赛弗特天体在内的活动星系核；（2）用IRAS源证认了一批W－R星系；（3）BATC巡天在一个星系周围发现了一个“环”。恒星层次天体：（1）河外星系中发现了一批超新星的一个新星，（2）发现了和用ROSAT源证认了一批激变变星，（4）BATC和超新星巡天发现了一批变星，（4）发现了若干新的δSct变星，（5）发现了一批Herbig-Haro天体，（6）发现了一批水脉泽。行星层次天体：（1）发现了大批小行星，特别是有些是近地不行星，（20发现若干彗星。     

Radiative association in Li+H revisited: the role of quasi-bound states

The role in radiative association of narrow resonances arising from quasi-bound levels is examined. For the rate coefficient the contribution from such levels is readily calculated in terms of the Einstein A -coefficient from bound rovibrational levels generated using a suitable alternative boundary condition. By locating systematically all the quasi-bound levels, an enhancement by more than an order of magnitude of the rate coefficient for Li(2s) + H(1s) association above about 600 K is obtained. The effect on the LiH abundance in the early Universe is no more than an increase of a factor of 3 for limited ranges of values of the redshift. For the Li(2p) state the effect of narrow resonances appears to have been taken into account in previous calculations.     

区域GPS网实时计算可降水量的若干问题

目前地基GPS气象学测得的可降水量 (PWV )精度好于 2mm ,但在利用区域GPS网实时计算每个测站上空的PWV时 ,要涉及到很多常规GPS资料处理时所忽略的问题 ,如需考虑数据处理软件和计算方式的选择、站坐标的确定和约束、轨道的使用方法、网外辅助站最佳数量的确定、海潮对实时计算PWV的影响以及实时应用于气象服务时的端部效应等问题。利用上海GPS综合应用网获取的 2 0 0 2年 6、7月份长江三角洲地区入梅前后的数据 ,分析了利用区域性的GPS网实时计算高精度的PWV时要解决的各种问题 ,探讨了其数据处理方案     

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