NUV Star Catalog from the Lunar-based Ultraviolet Telescope Survey: First Release

We present a star catalog extracted from the Lunar-based Ultraviolet Telescope （LUT） survey program. LUT＇s observable sky area is a circular belt around the Moon＇s north pole, and the survey program covers a preferred area of about 2400 deg2 which includes a region of the Galactic plane. The data are processed with an automatic pipeline which copes with stray light contamination, artificial sources, cosmic rays, flat field calibration, photometry and so on. In the first release version, the catalog provides high confidence sources which have been cross-identified with the Tycho-2 catalog. All the sources have signalto-noise ratio larger than 5, and the corresponding magnitude limit is typically 14.4 mag, but can be as deep as -16 mag if stray light contamination is at the lowest level. A total of 86 467 stars are recorded in the catalog. The full catalog in electronic form is available online.

A Comparison of BBN,ADTree and MLP in separating Quasars from Large Survey Catalogues

We compare the performance of Bayesian Belief Networks （BBN）, Multilayer Perception （MLP） networks and Alternating Decision Trees （ADtree） on separating quasars from stars with the database from the 2MASS and FIRST survey catalogs. Having a training sample of sources of known object types, the classifiers are trained to separate quasars from stars. By the statistical properties of the sample, the features important for classifica- tion are selected. We compare the classification results with and without feature selection. Experiments show that the results with feature selection are better than those without feature selection. From the high accuracy found, it is concluded that these automated methods are robust and effective for classifying point sources. They may all be applied to large survey projects （e.g. selecting input catalogs） and for other astronomical issues, such as the parameter measurement of stars and the redshift estimation of galaxies and quasars.

GeneticKNN:a weighted KNN approach supported by genetic algorithm for photometric redshift estimation of quasars

We combine K-nearest neighbors(KNN)with a genetic algorithm(GA)for photometric redshift estimation of quasars,short for GeneticKNN,which is a weighted KNN approach supported by a GA.This approach has two improvements compared to KNN:one is the feature weighted by GA;the other is that the predicted redshift is not the redshift average of K neighbors but the weighted average of median and mean of redshifts for K neighbors,i.e.p×zmedian+(1-p)×zmean.Based on the SDSS and SDSS-WISE quasar samples,we explore the performance of GeneticKNN for photometric redshift estimation,comparing with the other six traditional machine learning methods,i.e.the least absolute shrinkage and selection operator(LASSO),support vector regression(SVR),multi-layer perceptrons(MLP),XGBoost,KNN and random forest.KNN and random forest show their superiority.Considering the easy implementation of KNN,we make improvement on KNN as GeneticKNN and apply GeneticKNN on photometric redshift estimation of quasars.Finally the performance of GeneticKNN is better than that of LASSO,SVR,MLP,XGBoost,KNN and random forest for all cases.Moreover the accuracy is better with the additional WISE magnitudes for the same method.

A new strategy for estimating photometric redshifts of quasars

Based on the SDSS and SDSS-WISE quasar datasets, we put forward two schemes to estimate the photometric redshifts of quasars. Our schemes are based on the idea that the samples are firstly classified into subsamples by a classifier and then a photometric redshift estimation of different subsamples is performed by a regressor. Random Forest is adopted as the core algorithm of the classifiers, while Random Forest and k NN are applied as the key algorithms of regressors. The samples are divided into two subsamples and four subsamples, depending on the redshift distribution. The performances based on different samples, different algorithms and different schemes are compared. The experimental results indicate that the accuracy of photometric redshift estimation for the two schemes generally improves to some extent compared to the original scheme in terms of the percents in |△z|1+zi< 0.1 and |△z|1+zi<0.2 and mean absolute error. Only given the running speed, k NN shows its superiority to Random Forest. The performance of Random Forest is a little better than or comparable to that of k NN with the two datasets. The accuracy based on the SDSS-WISE sample outperforms that based on the SDSS sample no matter by k NN or by Random Rorest. More information from more bands is considered and helpful to improve the accuracy of photometric redshift estimation. Evidently, it can be found that our strategy to estimate photometric redshift is applicable and may be applied to other datasets or other kinds of objects. Only talking about the percent in |△z|1+zi<0.3, there is still large room for further improvement in the photometric redshift estimation.

Astronomical data fusion tool based on PostgreSQL

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.

Discovery of 2716 hot emission-line stars from LAMOST DR5

We present a catalog of 3339 hot emission-line stars(ELSs)identified from 451695 O,B and A type spectra,provided by LAMOST Data Release 5(DR5).We developed an automated Python routine that identified 5437 spectra having a peak between 6561 and 6568.False detections and bad spectra were removed,leaving 4138 good emission-line spectra of 3339 unique ELSs.We re-estimated the spectral types of 3307 spectra as the LAMOST Stellar Parameter Pipeline(LASP)did not provide accurate spectral types for these emission-line spectra.As Herbig Ae/Be stars exhibit higher excess in near-infrared and mid-infrared wavelengths than classical Ae/Be stars,we relied on 2 MASS and WISE photometry to distinguish them.Finally,we report 1089 classical Be,233 classical Ae and 56 Herbig Ae/Be stars identified from LAMOST DR5.In addition,928 B[em]/A[em]stars and 240 CAe/CBe potential candidates are identified.From our sample of 3339 hot ELSs,2716 ELSs identified in this work do not have any record in the SIMBAD database and they can be considered as new detections.Identification of such a large homogeneous set of emission-line spectra will help the community study the emission phenomenon in detail without worrying about the inherent biases when compiling from various sources.

First PPMXL photometric analysis of open cluster Ruprecht 15

We present the first in a series studying the astrophysical parameters of open clusters using the PPMXL＊ database whose data are applied to study Ruprecht 15. The astrophysical parameters of Ruprecht 15 have been estimated for the first time.