Quasars with redshifts greater than 4 are rare, and can be used to probe the structure and evolution of the early universe. Here we report the discovery of six new quasars with i-band magnitudes brighter than 19.5 and redshifts between 2.4 and 4.6 from spectroscopy with the Yunnan Faint Object Spectrograph and Camera (YFOSC) at the Lijiang 2.4 m telescope in February, 2012. These quasars are in the list of z > 3.6 quasar candidates selected by using our proposed J K/i Y criterion and the photometric redshift estimations from the SDSS optical and UKIDSS near-IR photometric data. Nine candidates were observed by YFOSC, and five among six new quasars were identified as z > 3.6 quasars. One of the other three objects was identified as a star and the other two were unidentified due to the lower signal-to-noise ratio of their spectra. This is the first time that z > 4 quasars have been discovered using a telescope in China. Thanks to the Chinese Telescope Access Program (TAP), the redshift of 4.6 for one of these quasars was confirmed by the Multiple Mirror Telescope (MMT) Red Channel spectroscopy. The continuum and emission line properties of these six quasars, as well as their central black hole masses and Eddington ratios, were obtained.
With the application of advanced astronomical technologies, equipments and methods all over the world, astronomical observations cover the range from radio, infrared, visible light, ultraviolet, X-ray and gamma-ray bands, and enter into the era of full wavelength astronomy. How to effectively integrate data from different ground- and space-based observation equipments, different observers, different bands and different observation times, requires data fusion technology. In this paper we introduce a cross-match tool that is developed in the Python language, is based on the PostgreSQL database and uses Q3C as the core index, facilitating the cross-match work of massive astronomical data. It provides four different cross- match functions, namely： （I） cross-match of the custom error range; （II） cross-match of catalog errors; （III） cross-match based on the elliptic error range; （IV） cross-match of the nearest neighbor algorithm. The resulting cross-matched set provides a good foundation for subsequent data mining and statistics based on multiwavelength data. The most advantageous aspect of this tool is a user-oriented tool applied locally by users. By means of this tool, users can easily create their own databases, manage their own data and cross- match databases according to their requirements. In addition, this tool is also able to transfer data from one database into another database. More importantly, it is easy to get started with the tool and it can be used by astronomers without writing any code.
AutoClass is an unsupervised Bayesian classification approach which seeks a maximum posterior probability classification for determining the optimal classes in large data sets. Using stellar photometric data from the Sloan Digital Sky Survey (SDSS) data release 7 (DR7), we utilize AutoClass to select non-stellar objects from this sample in order to build a pure stellar sample. For this purpose, the differences between PSF (point spread function) magnitudes and model magnitudes in five wavebands are taken as the input of AutoClass. Through clustering analysis of this sample by AutoClass, 617 non-stellar candidates are found. These candidates are identified by NED and SIMBAD databases. Most of the identified sources (13 from SIMBAD and 28 from NED respectively) are extragalactic sources (e.g., galaxies, HII, radio sources, infrared sources), some are peculiar stars (e.g., supernovas), and very few are normal stars. The extragalactic sources and peculiar stars of the identified objects occupy 94.1%. The result indicates that this method is an effective and robust clustering algorithm to find non-stellar objects and peculiar stars from the total stellar sample.
We integrate k-Nearest Neighbors(kNN) into Support Vector Machine(SVM) and create a new method called SVM-kNN.SVM-kNN strengthens the generalization ability of SVM and apply kNN to correct some forecast errors of SVM and improve the forecast accuracy.In addition,it can give the prediction probability of any quasar candidate through counting the nearest neighbors of that candidate which is produced by kNN.Applying photometric data of stars and quasars with spectral classification from SDSS DR7 and considering limiting magnitude error is less than 0.1,SVM-kNN and SVM reach much higher performance that all the classification metrics of quasar selection are above 97.0%.Apparently,the performance of SVM-kNN has slighter improvement than that of SVM.Therefore SVM-kNN is such a competitive and promising approach that can be used to construct the targeting catalogue of quasar candidates for large sky surveys.
