大开眼界看朝阳

<正>来中国留学已经一段时间了,去过的地方也不少,可是给我留下最深印象的却是一个很多人都不知道的城市——辽宁的朝阳。在辽宁生活的时间里,我一直觉得辽宁是一个很普通没有什么吸引力的地方。但参加了沈阳师范大学特色班的"留学生辽宁文化考察活动"以后,我忽然发现,原来在这     

An unsupervised, ensemble clustering algorithm: A new approach for classification of X-ray sources

A large volume of CCD X-ray spectra is being generated by the Chandra X-ray Observatory (Chandra) and XMM-Newton. Automated spectral analysis and classification methods can aid in sorting, characterizing, and classifying this large volume of CCD X-ray spectra in a non-parametric fashion, complementary to current parametric model fits. We have developed an algorithm that uses multivariate statistical techniques, including an ensemble clustering method, applied for the first time for X-ray spectral classification. The algorithm uses spectral data to group similar discrete sources of X-ray emission by placing the X-ray sources in a three-dimensional spectral sequence and then grouping the ordered sources into clusters based on their spectra. This new method can handle large quantities of data and operate independently of the requirement of spectral source models and a priori knowledge concerning the nature of the sources (i.e., young stars, interacting binaries, active galactic nuclei). We apply the method to Chandra imaging spectroscopy of the young stellar clusters in the Orion Nebula Cluster and the NGC 1333 star formation region.