Photometric Calibration of the Lunar-based Ultraviolet Telescope for Its First Six Months of Operation on the Lunar Surface

We describe the photometric calibration of the Lunar-based Ultraviolet Telescope（LUT）, the first robotic astronomical telescope working on the lunar surface, for its first six months of operation on the lunar surface. Two spectral datasets（set A and B） from near-ultraviolet（NUV） to the optical band were constructed with 44 International Ultraviolet Explorer（IUE） standards, because of the LUT＇s relatively wide wavelength coverage. Set A was obtained by extrapolating the IUE NUV spectra（λ 〈 3200 ） to the optical band based upon the theoretical spectra of stellar atmosphere models. Set B was composed of theoretical spectra from 2000 to 8000 extracted from the same model grid. In total, seven standards have been observed in15 observational runs until May 2014. The calibration results show that the photometric performance of LUT is highly stable in its first six months of operation. The magnitude zero points obtained from the two spectral datasets are also consistent with each other, i.e., zp = 17.54 ± 0.09 mag（set A） and zp = 17.52 ± 0.07 mag（set B）.

Spectral classification of stars based on LAMOST spectra

In this work, we select spectra of stars with high signal-to-noise ratio from LAMOST data and map their MK classes to the spectral features. The equivalent widths of prominent spectral lines, which play a similar role as multi-color photometry, form a clean stellar locus well ordered by MK classes. The advantage of the stellar locus in line indices is that it gives a natural and continuous classification of stars consistent with either broadly used MK classes or stellar astrophysical parameters. We also employ an SVM-based classification algorithm to assign MK classes to LAMOST stellar spectra. We find that the completenesses of the classifications are up to 90% for A and G type stars, but they are down to about 50% for OB and K type stars. About 40% of the OB and K type stars are mis-classified as A and G type stars,respectively. This is likely due to the difference in the spectral features between late B type and early A type stars or between late G and early K type stars being very weak. The relatively poor performance of the automatic MK classification with SVM suggests that the direct use of line indices to classify stars is likely a more preferable choice.

Photometric redshift estimation for quasars by integration of KNN and SVM

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches （such as the k-nearest neighbor （KNN） algorithm）. In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine （SVM） methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding Zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of Zphot E [0.3, 1.2] and the other in the range of Zphot E [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey （SDSS） quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different /△z/ ranges and root mean square （rms） error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN （71.96%, 83.78%, 89.73% and 0.204）.

General modeling and optimization technique for real-world earth observation satellite scheduling

Over the last two decades,many modeling and optimization techniques have been developed for earth observation satellite(EOS)scheduling problems,but few of them show good generality to be engineered in realworld applications.This study proposes a general modeling and optimization technique for common and real-world EOS scheduling cases;it includes a decoupled framework,a general modeling method,and an easy-to-use algorithm library.In this technique,a framework that decouples the modeling,constraints,and optimization of EOS scheduling problems is built.With this framework,the EOS scheduling problems are appropriately modeled in a general manner,where the executable opportunity,another format of the well-known visible time window per EOS operation,is viewed as a selectable resource to be optimized.On this basis,10 types of optimization algorithms,such as Tabu search and genetic algorithm,and a parallel competitive memetic algorithm,are developed.For simplified EOS scheduling problems,the proposed technique shows better performance in applicability and effectiveness than the state-of-the-art algorithms.In addition,a complicatedly constrained real-world benchmark exampled by a four-EOS Chinese commercial constellation is provided,and the technique is qualified and outperforms the in-use scheduling system by more than 50%.

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 1.2, and the test set of stellar spectra from Pickles' atlas and SDSS spectra of normal galaxies with SNR of 13. Experiments show that our SMM-RBFNN is more efficient in both the training and testing stages than the BPNN (back propagation neural networks), and more importantly, it can achieve a good classification accuracy of 99.22% and 96.52%, respectively for stars and normal galaxies.

Optical spectroscopy and photometry of main-belt asteroids with a high orbital inclination

We carried out low-resolution optical spectroscopy of 51 main-belt asteroids, most of which have highly-inclined orbits. They are selected from D-type candidates in the SDSS-MOC 4 catalog. Using the University of Hawaii 2.2 m telescope and the Inter-University Centre for Astronomy and Astrophysics 2 m telescope in India, we determined the spectral types of 38 asteroids. Among them, eight asteroids were classified as D-type asteroids. Fractions of D-type asteroids are 3.0 ± 1.1% for low orbital inclination main-belt asteroids and 7.3 ± 2.0% for high orbital inclination main-belt asteroids. The results of our study indicate that some D-type asteroids were formed within the ecliptic region between the main belt and Jupiter, and were then perturbed by Jupiter.

Hα measurements using DEFPOS/RTT150 Telescope: Instrumentation and observations

To investigate the physical properties of HII regions and some PNe about 4＇ in size, a DEFPOS Fabry-Perot spectrometer has been redesigned and set up at the coude exit of the 150cm RTT150 telescope （f/48） at TUBITAK National Observatory （Antalya/Baklrhtepe, Turkey）. The spectrometer has a 4＇ circular field of view and a velocity resolution of 27.27 km s^-1 （a spectral resolving power of 11000） over a 200 km s^-1 spectral window near Hα. This work presents the details of the newly redesigned instrument for coude observations, the data reduction techniques and finally presents some physical results of our new Hα observations selected from the Reynolds et al. （2005） and Fich et al. （1990） papers. The DEFPOS system has been fully tested and the first observations of HII regions and PNe in the Galaxy are used to illustrate the power of the instrument. We feel that our first Fabry-Perot observations can provide a powerful tool for the study of objects with small angular size. In the future, we are planning to prepare a catalog including some physical properties such as radial velocity, line width, and intensity of some PNe and HII regions close to the 4＇ field of view.

Automated removal of stripe interference in full-disk solar images

The quality of full-disk solar Hα images is significantly degraded by stripe interference. In this paper, to improve the analysis of morphological evolution, a robust solution for stripe interference removal in a partial full-disk solar Hα image is proposed. The full-disk solar image is decomposed into a set of support value images on different scales by convolving the image with a sequence of multiscale support value filters, which are calculated from the mapped least-squares support vector machines （LS-SVMs）. To match the resolution of the support value images, a scale-adaptive LS-SVM regression model is used to remove stripe interference from the support value images. We have demonstrated the advantages of our method on solar Hα images taken in 2001-2002 at the Huairou Solar Observing Station. Our experimental results show that our method can remove the stripe interference well in solar Hα images and the restored image can be used in morphology researches.

Global Mg/Si and Al/Si Distributions on the Lunar Surface Derived from Chang'E-2 X-ray Spectrometer

The technique of X-ray fluorescence remote sensing plays a significant role in research related to the chemical compositions of the Moon. Here we describe the data analysis method for China＇s Chang＇E-2 X-ray spectrometer in detail and present the preliminary results about the first global Mg/Si and Al/Si maps of the lunar surface. Our results show that the distributions of Mg/Si and Al/Si correlate well with terrains on the Moon. The higher Mg/Si ratio corresponds to the mare regions while the lower value corresponds to the highland terrains. The map of the Al/Si ratio shows a reversed distribution compared with the map of the Mg/Si ratio.

Scattering Law Analysis Based on Hapke and Lommel-Seeliger Models for Asteroidal Taxonomy

In deriving the physical properties of asteroids from their photometric data, the scattering law plays an important role, although the shape variations of asteroids result in the main variations in lightcurves. By following the physical behaviors of light reflections, Hapke et al. deduced complex functions to represent the scattering process, however, it is very hard to accurately simulate the surface scattering law in reality. For simplicity, other numerical scattering models are presented for efficiently calculating the physical properties of asteroids, such as the Lommel-Seeliger （LS） model. In this article, these two models are compared numerically. It is found that in some numerical applications the LS model in simple form with four parameters can be exploited to replace the Hapke model in complex form with five parameters. Furthermore, the generated synthetic lightcurves by the Cellinoid shape model also show that the LS model can perform as well as the Hapke model in the inversion process. Finally, by applying the Principal Component Analysis （PCA） technique to the parameters of the LS model, we present an efficient method to classify C and S type asteroids, instead of the conventional method using the parameters of the Hapke model.