Kinematics and stellar population properties of the Andromeda galaxy by using the spectroscopic observations of the Guoshoujing Telescope

The Andromeda galaxy was observed by the Guoshoujing Telescope （formerly named the Large Sky Area Multi-Object Fiber Spectroscopic Telescope -- LAMOST）, during the 2009 commissioning phase. Due to the absence of standard stars for flux calibration, we use the photometric data of 15 intermediate bands in the Beijing-Arizona-Taipei-Connecticut （BATC） survey to calibrate the spectra. In to- tal, 59 spectra located in the bulge and disk of the galaxy are obtained. Kinematic and stellar population properties of the stellar content are derived with these spectra. We obtain the global velocity field and calculate corresponding rotation velocities out to about 7 kpc along the major axis. These rotation velocity measurements comple- ment those of the gas content, such as the H I and CO. The radial velocity dispersion shows that the stars in the bulge are more dynamically thermal and the disk is more rotationally-supported. The age distribution shows that the bulge was formed about 12 Gyr ago, the disk is relatively younger and the ages of some regions along the spi- ral arms can reach as young as about 1 Gyr. These young stellar populations have a relatively richer abundance and larger reddening. The overall average metallicity of the galaxy approximates the solar metallicity and a very weak abundance gradient is gained. The reddening map gives a picture of a dust-free bulge and a distinct dusty ring in the disk.

Relative flux calibration for the Guoshoujing Telescope (LAMOST)

This paper presents a relative flux calibration method for the Guoshoujing Telescope （LAMOST）, which may be applied to connect a blue spectrum to a red spectrum to build the whole spectrum across the total wavelength range （3700 ～ 9000 A）. In each spectrograph, we estimate the effective temperatures of selected stars using a grid of spectral line indices in the blue spectral range and a comparison with stellar atmosphere models. For each spectrograph, stars of types A and F are selected as pseudo-standard stars, and the theoretical spectra are used to calibrate both the blue （3700 ～ 5900 A） and red spectrograph arms （5700 ～ 9000 A）. Then the spectral response function for these pseudo-standard stars could be used to correct the raw spectra provided by the other fibers of the spectrograph, after a fiber efficiency function has been derived from twilight flat-field exposures. A key problem in this method is the fitting of a pseudo stellar continuum, so we also give a detailed description of this step. The method is tested by comparing a small sample of LAMOST spectra calibrated in this way on stars also observed by the Sloan Digital Sky Survey. The result shows that the T eff estimation and relative flux calibration method are adequate.

Steps Towards a Fully Automated Classification and Redshiftmeasurement Pipeline for LAMOST Spectra. I.Continuum level and wavelength estimation for galaxies

The Large Sky-Area Multi-Object Spectroscopic Telescope (LAMOST) under construction by the National Astronomical Observatories will yield up to four thousand multi-fiber spectra of stars and galaxies per field. The present series of papers describes the automated data-reduction pipeline currently being designed in order to cope with the anticipated flood of spectrographic data. In this preliminary paper, we present an automated method for estimating the continuum level, the positions of strong lines and the 4000 A break in galaxy spectra. In order to obtain detailed information on the continuum, we use a wavelet filter bank. After continuum fitting, our software searches for a 4000 A break and distinguishes between emission-line galaxies (ELGs) and non-ELGs according to whether the break is small or large. It then searches for strong lines and measures the intensities of emission lines and the equivalent widths of absorption lines. For non-ELGs, the absorption lines are identified automatically yielding redshift measurements.

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.

Automated Stellar Classification for Large Surveys with EKF and RBF Neural Networks

An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification. Experiments with real data have shown that the correct classification rate can reach as high as 93%, which is quite satisfactory. When different system models are selected for the extended Kalman filter, the classification results are relatively stable. It is shown that for this particular case the result using extended Kalman filter is better than using principal component analysis.

Multidisciplinary and multiscale nanoscience research roadmap based on large scientific facilities

With the advancement of modern science and technology, large scientific facilities are increasingly oriented toward demand and application, and can be used for basic research as well as serving multiple disciplines. Developing large scientific facilities and related analytical technologies enhances understanding of large scientific facilities and popularizes their application in research across multiple disciplines. The combination of light or neutron sources from large scientific facilities and advanced analytical technologies can be achieved for materials structure information, dynamics study of chemical reactions, high dissociation of biomolecules, 3D visualization of energy materials or biological samples, etc. We first introduce the progress of domestic large scientific facilities of synchrotron radiation(SR) and free electron lasers(FELs) with different wavelengths and neutron sources.We further discuss the comparison between Chinese and typical foreign facilities in X-ray radiation from X-ray tubes, synchrotrons, X-ray FELs, and neutron sources based on physical parameters of light and neutron sources. In addition, we focus on the technological progress and perspectives combined with advanced X-ray radiation and neutron sources of large scientific facilities in China, especially in the nanoscience fields of energy catalysis and biological science. We hope that this roadmap will provide references on technology and methods to experimental users, as well as prospects for future development of technologies based on large research infrastructure facilities. Comprehensive studies and guidelines for basic research to practical application in various disciplines can be made with the assistance of large scientific facilities.