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     

超新星光谱研究的进展：观测

介绍了超新星完整样品的分类概况，依次转述了各类超新星光谱观测的新进展。两颗特殊Ｉａ型超新星－ＳＮ１９９１Ｔ和ＳＮ１９９１ｂｇ一的发现对原先认为Ｉａ型超新星是均质的观点提出了挑战，对Ｉｂ／Ｉｃ型超新星应加以特别关注，ＩＩ型超新星ＳＮ１９９３Ｊ和ＳＮ１９８７Ｋ由光极大时的ＩＩ型演化星云类似于Ｉｂ／Ｉｃ的光谱，对传统的超新星Ｉ型和ＩＩ型的区分提出了质疑，对某些特殊ＩＩ型超新星也许列为“ＩＩｎ”型，其Ｈα