Altering the CO2 electroreduction pathways towards C1 or C2+ products via engineering the strength of interfacial Cu-O bond.

Copper (Cu)-based catalysts have established their unique capability for yielding wide value-added products from CO2. Herein, we demonstrate that the pathways of the electrocatalytic CO2 reduction reaction (CO2RR) can be rationally altered toward C1 or C2+ products by simply optimizing the coordination of Cu with O-containing organic species (squarate (C4O4) and cyclohexanhexanone (C6O6)). It is revealed that the strength of Cu-O bonds can significantly affect the morphologies and electronic structures of derived Cu catalysts, resulting in the distinct behaviors during CO2RR. Specifically, the C6O6-Cu catalysts made up from organized nanodomains shows a dominant C1 pathway with a total Faradaic efficiency (FE) of 63.7% at -1.0 V (versus reversible hydrogen electrode, RHE). In comparison, the C4O4-Cu with an about perfect crystalline structure results in uniformly dispersed Cu-atoms, showing a notable FE of 65.8% for C2+ products with enhanced capability of C-C coupling. The latter system also shows stable operation over at least 10 h with a high current density of 205.1 mA cm-2 at -1.0 VRHE, i.e. is already at the boarder of practical relevance. This study sheds light on the rational design of Cu-based catalysts for directing the CO2RR reaction pathway.

The evolution of hot Jupiters revealed by the age distribution of their host stars.

The unexpected discovery of hot Jupiters challenged the classical theory of planet formation inspired by our solar system. Until now, the origin and evolution of hot Jupiters are still uncertain. Determining their age distribution and temporal evolution can provide more clues into the mechanism of their formation and subsequent evolution. Using a sample of 383 giant planets around Sun-like stars collected from the kinematic catalogs of the Planets Across Space and Time project, we find that hot Jupiters are preferentially hosted by relatively younger stars in the Galactic thin disk. We subsequently find that the frequency of hot Jupiters declines with age as [Formula: see text]. In contrast, the frequency of warm/cold Jupiters shows no significant dependence on age. Such a trend is expected from the tidal evolution of hot Jupiters' orbits, and our result offers supporting evidence using a large sample. We also perform a joint analysis on the planet frequencies in the stellar age-metallicity plane. The result suggests that the frequencies of hot Jupiters and warm/cold Jupiters, after removing the age dependence are both correlated with stellar metallicities as [Formula: see text] and [Formula: see text], respectively. Moreover, we show that the above correlations can explain the bulk of the discrepancy in hot Jupiter frequencies inferred from the transit and radial velocity (RV) surveys, given that RV targets tend to be more metal-rich and younger than transits.

SpectrumVA: Visual Analysis of Astronomical Spectra for Facilitating Classification Inspection.

In astronomical spectral analysis, class recognition is essential and fundamental for subsequent scientific research. The experts often perform the visual inspection after automatic classification to deal with low-quality spectra to improve accuracy. However, given the enormous spectral volume and inadequacy of the current inspection practice, such inspection is tedious and time-consuming. This paper presents a visual analytics system named SpectrumVA to promote the efficiency of visual inspection while guaranteeing accuracy. We abstract inspection as a visual parameter space analysis process, using redshifts and spectral lines as parameters. Different navigation strategies are employed in the "selection-inspection-promotion" workflow. At the selection stage, we help the experts identify a spectrum of interest through spectral representations and auxiliary information. Several possible redshifts and corresponding important spectral lines are also recommended through a global-to-local strategy to provide an appropriate entry point for the inspection. The inspection stage adopts a variety of instant visual feedback to help the experts adjust the redshift and select spectral lines in an informed trial-and-error manner. Similar spectra to the inspected one rather than different ones are visualized at the promotion stage, making the inspection process more fluent. We demonstrate the effectiveness of SpectrumVA through a quantitative algorithmic assessment, a case study, interviews with domain experts, and a user study.

Overview of the LAMOST survey in the first decade.

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), also known as the Guoshoujing Telescope, is a major national scientific facility for astronomical research located in Xinglong, China. Beginning with a pilot survey in 2011, LAMOST has been surveying the night sky for more than 10 years. The LAMOST survey covers various objects in the Universe, from normal stars to peculiar ones, from the Milky Way to other galaxies, and from stellar black holes and their companions to quasars that ignite ancient galaxies. Until the latest data release 8, the LAMOST survey has released spectra for more than 10 million stars, ∼220,000 galaxies, and ∼71,000 quasars. With this largest celestial spectra database ever constructed, LAMOST has helped astronomers to deepen their understanding of the Universe, especially for our Milky Way galaxy and the millions of stars within it. In this article, we briefly review the characteristics, observations, and scientific achievements of LAMOST. In particular, we show how astrophysical knowledge about the Milky Way has been improved by LAMOST data.

Dry eye induced by exposure to cigarette smoke pollution: An in vivo and in vitro study.

Exposure to cigarette smoke (CS) pollution has previously associated with dry eye symptoms but without detailed experimental data and elucidation of the mechanism. We aimed to evaluate the effects of CS on the ocular surfaces of mice and the extraction of DMSO lipid-soluble cigarette smoke particles (DCSP) on cultured human corneal epithelial cells (HCECs), and explore to elucidate the probable mechanism. C57BL mice were exposed to CS challenging. In vivo clinical evaluations, including corneal fluorescein staining, tear film break-up time, and confocal microscopic observations, were performed before exposure and post-exposure. At the end of the in vivo study, changes in corneal and conjunctival histology, corneal ultrastructure, and conjunctival goblet cell intensity were examined, expression of TUNLE and Ki67 in tissue were also detected. In vitro, cell confluence and caspase3/7 were assessed in DCSP treated HCECs. Production of TNF-α, IL-1ß and IL-6, activation of NF-κB and Ki67 were evaluated by means of ELISA and Western blot respectively in HCECs cultured with 0.6 µL/mL DCSP. We found that longer-term CS exposure induced dry eye symptoms in mice. Additionally, corneal and conjunctival epithelial damage occurred, the corneal ultrastructure changed, and the density of goblet cells decreased. Apoptosis and Ki67 increased in both the conjunctiva and the cornea of CS-exposed animals. Furthermore, although DCSP inhibited the proliferation of HCECs, expression of Ki67 increased and apoptosis was only induced significantly by 2.0 µL/mL DCSP. The release of IL-1ß and IL-6, activation of NF-κB were prompted by DCSP. The results indicated that CS is toxic to the ocular surface of mice and HCECs. Longer-term CS exposure in mice stimulates ocular surface changes that resemble those observed with dry eye. The mechanism may relate to inflammation and activation of NF-κB. In this study, we established a novel animal model to study dry eye, with the experimental data and elucidation of mechanism facilitating further research.

Amyloid Beta Deposition Could Cause Corneal Epithelial Cell Degeneration Associated with Increasing Apoptosis in APPswePS1 Transgenic Mice.

OBJECTIVE: To investigate the expression of amyloid precursor protein (APP) and amyloid beta (Aß) in cornea and further explore the pathological and ultrastructural changes in corneal epithelium in APPswePS1 transgenic mice. METHODS: Twelve wild type mice were grouped into control group and twelve TgAPPswePS1 mice at least 8 months old were grouped into the young experiment group (Tg-8M group), and another twelve transgenic mice at least 15 months old were selected into the aged experiment group (Tg-15M group). The pathological degeneration, ultrastructural changes, and the expression of APP, Aß deposition, and the TUNEL reaction in corneal epithelial cells were observed. Western blot analysis was performed to determine expression levels of APP and Aß with scraped epithelial debridement. All the results were quantified and analyzed. RESULTS: In transgenic mice, the H&E-stained cornea sections demonstrated histopathological changes in corneal epithelial cells with irregular arrangement and the number of cell layers decreased, while normal structure observed in controls. In Tg-15M group, the corneal epithelial cell displaced a significant number of intracellular vacuoles with 1-2 cell layers left. Transmission electron microscopy (TEM) further confirmed the dramatic degeneration in corneal epithelium, the microvilli suffered degenerative changes and found with typical fingerpoint-like morphology in controls; however, microspike-like in Tg-15M group, and the number of microvilli decreased considerabely. An APP-positive immunoreaction was detected with a diffuse pattern in the corneal epithelial cells layer, about 3.122 ± 0.596 and 7.372 ± 0.936 fold changes in Tg-8M and Tg-15M groups, respectively, as compared with controls. On corneal flatmount, Aß deposition found a diffuse pattern in the cytoplasm by fluorescence staining in TgAPPswePS1 with significantly increasing as compared with the controls, but no plaque was found. The apoptosis of TUNEL cells were observed in TgAPPswePS1 mice and increased 16.329 ± 3.542 fold changes in Tg-15M group as compared with controls. CONCLUSION: The APP expression and Aß deposition might cause cornea epithelial cells degeneration in TgAPPswePS1 mice, associated with apoptosis in basal lamina cells.

LAMOST telescope reveals that Neptunian cousins of hot Jupiters are mostly single offspring of stars that are rich in heavy elements.

We discover a population of short-period, Neptune-size planets sharing key similarities with hot Jupiters: both populations are preferentially hosted by metal-rich stars, and both are preferentially found in Kepler systems with single-transiting planets. We use accurate Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Data Release 4 (DR4) stellar parameters for main-sequence stars to study the distributions of short-period [Formula: see text] Kepler planets as a function of host star metallicity. The radius distribution of planets around metal-rich stars is more "puffed up" compared with that around metal-poor hosts. In two period-radius regimes, planets preferentially reside around metal-rich stars, while there are hardly any planets around metal-poor stars. One is the well-known hot Jupiters, and the other one is a population of Neptune-size planets ([Formula: see text]), dubbed "Hoptunes." Also like hot Jupiters, Hoptunes occur more frequently in systems with single-transiting planets although the fraction of Hoptunes occurring in multiples is larger than that of hot Jupiters. About [Formula: see text] of solar-type stars host Hoptunes, and the frequencies of Hoptunes and hot Jupiters increase with consistent trends as a function of [Fe/H]. In the planet radius distribution, hot Jupiters and Hoptunes are separated by a "valley" at approximately Saturn size (in the range of [Formula: see text]), and this "hot-Saturn valley" represents approximately an order-of-magnitude decrease in planet frequency compared with hot Jupiters and Hoptunes. The empirical "kinship" between Hoptunes and hot Jupiters suggests likely common processes (migration and/or formation) responsible for their existence.

Exoplanet orbital eccentricities derived from LAMOST-Kepler analysis.

The nearly circular (mean eccentricity [Formula: see text]) and coplanar (mean mutual inclination [Formula: see text]) orbits of the solar system planets motivated Kant and Laplace to hypothesize that planets are formed in disks, which has developed into the widely accepted theory of planet formation. The first several hundred extrasolar planets (mostly Jovian) discovered using the radial velocity (RV) technique are commonly on eccentric orbits ([Formula: see text]). This raises a fundamental question: Are the solar system and its formation special? The Kepler mission has found thousands of transiting planets dominated by sub-Neptunes, but most of their orbital eccentricities remain unknown. By using the precise spectroscopic host star parameters from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) observations, we measure the eccentricity distributions for a large (698) and homogeneous Kepler planet sample with transit duration statistics. Nearly half of the planets are in systems with single transiting planets (singles), whereas the other half are multiple transiting planets (multiples). We find an eccentricity dichotomy: on average, Kepler singles are on eccentric orbits with [Formula: see text] 0.3, whereas the multiples are on nearly circular [Formula: see text] and coplanar [Formula: see text] degree) orbits similar to those of the solar system planets. Our results are consistent with previous studies of smaller samples and individual systems. We also show that Kepler multiples and solar system objects follow a common relation [[Formula: see text](1-2)[Formula: see text]] between mean eccentricities and mutual inclinations. The prevalence of circular orbits and the common relation may imply that the solar system is not so atypical in the galaxy after all.

[A New Redshift Measuring Method for Low-Quality Galaxy Spectra Based on Multi Resolution Fusion Distance].

The redshift measurement of galaxy spectrum is a key issue in large astronomical spectral survey. Its goal is to extract the redshift from spectrum, which is caused the Doppler Effect. With the development of the extragalactic sky survey project, the distance (redshift) of the observed targets is becoming further. As a result, the magnitude of the observed objects becomes darker and the spectral quality becomes poorer. Therefore, how to effectively and accurately measure the redshift from these low quality spectra is becoming an important problem in the extragalactic survey. Considering the spectral features and the data character, a new definition of multi-resolution fusion distance for low quality spectra is proposed. In this paper, we put forward a redshift measuring method for low quality galaxy spectra. This method combines the spectral features with different resolutions. The template spectrum and the spectrum to measure are reduced to the resolution and then a distance is computed by combining the offset of the above two spectra in different wavelengths. Then, a fusion distance is weighted averaged from the distances with different resolutions. In this paper, the effect of signal-to-noise ratio (SNR) on the measuring accuracy of the proposed method is discussed. The measuring accuracy is larger than 90% when the SNR is larger than 5. A large number of experiments show that the method proposed in this paper is very efficient in measuring the redshift of the low-quality galaxy spectra and the measuring error has nothing to do with the redshift value. The proposed method can be applied in redshift measurement of galaxies for the large-scale survey data.

[Data Mining for CVs Spectra in LAMOST-DR1].

LAMOST-DR1 is the first data released by Guoshoujing telescop, which has the largest number of stellar spectra in the world at present. The data set provides the data source for searching for special and rare celestial objects like cataclysmic variable stars.Meanwhile, it requires more advanced astronomical data processing methods and techniques. A data mining method for cataclysmic variable spectra in massive spectra data is proposed in this paper. Different types of celestial spectra show obvious difference in the feature space constructed with Laplacian Eigenmap method. The parameters of artificial neural network are optimized with particle swarm optimization method and the total LAMOST-DR1 data is processed. 7 cataclysmic variable star spectra are found in the experiment including 2 dwarf nova, 2 nova like variables and a highly polarized AM Her type. The newly found spectra enrich the current cataclysmic variable spectra library. The experiment is the first attempt of searching for cataclysmic variable star spectra with Guoshoujing telescope data and the results show that our approach is feasible in LAMOST data. This method is also applicable for mining other special celestial objects in sky survey telescope data.

[A Method of Stellar Spectral Classification Based on Map/Reduce Distributed Computing].

Celestial spectrum contains a great deal of astrophysical information. Through the analysis of spectra, people can get the physical information of celestial bodies, as well as their chemical composition and atmospheric parameters. With the implementation of LAMOST, SDSS telescopes and other large-scale surveys, massive spectral data will be produced, especially along with the formal operation of LAMOST, 2 000 to 4 000 spectral data will be generated each observation night. It requires more efficient processing technology to cope with such massive spectra. Automatic classification of stellar spectra is a basic content of spectral processing. The main purpose of this paper is to research the automatic classification of massive stellar spectra. The Lick index is a set of standard indices defined in astronomical spectra to describe the spectral intensity of spectral lines, which represent the physical characteristics of spectra. Lick index is a relatively wide spectral characteristics, each line index is named after the most prominent absorption line. In this paper, the Bayesian method is used to classify stellar spectra based on Lick line index, which divides stellar spectra to three subtypes: F, G, K. First of all, Lick line index of spectra is calculated as the characteristic vector of spectra, and then Bayesian method is used to classify these spectra. For massive spectra, the computation of Lick indices and the spectral classification using Bayesian decision method are implemented on Hadoop. With use of the high throughput and good fault tolerance of HDFS, combined with the advantages of MapReduce parallel programming model, the efficiency of analysis and processing for massive spectral data have been improved significantly. The main innovative contributions of this thesis are as follows. (1) Using Lick indices as the characteristic to classify stellar spectra based on Bayesian decision method. (2) Implementing parallel computation of Lick indices and parallel classification of stellar spectra using Bayesian based on Hadoop MapReduce distributed computing framework.

[A Method to Estimate Metal Abundance from Stellar Spectra Using Ca Line Index].

This paper presents a method to estimate stellar metallicity based on BP neural network and Ca line index. This method trains a BP ANN model from SDSS/SEGUE stellar spectra and parameters provided by SSPP. The values of Teff and the line index of Ca lines are the input of network while the [Fe/H] values are the oputput of the network. A set of samples are resampled from the set of all and then a network model is trained. The network can be used to predict the stellar metallicity from low-resolution spsectra. The experiment shows that the proposed method can accurately and effectively measure the [Fe/H] from the stellar spectra.

Protective effects of extracts from Pomegranate peels and seeds on liver fibrosis induced by carbon tetrachloride in rats.

BACKGROUND: Liver fibrosis is a feature in the majority of chronic liver diseases and oxidative stress is considered to be its main pathogenic mechanism. Antioxidants including vitamin E, are effective in preventing liver fibrogenesis. Several plant-drived antioxidants, such as silymarin, baicalin, beicalein, quercetin, apigenin, were shown to interfere with liver fibrogenesis. The antioxidans above are polyphenols, flavonoids or structurally related compounds which are the main chemical components of Pomegranate peels and seeds, and the antioxidant activity of Pomegranate peels and seeds have been verified. Here we investigated whether the extracts of pomegranate peels (EPP) and seeds (EPS) have preventive efficacy on liver fibrosis induced by carbon tetrachloride (CCl4) in rats and explored its possible mechanisms. METHODS: The animal model was established by injection with 50 % CCl4 subcutaneously in male wistar rats twice a week for four weeks. Meanwhile, EPP and EPS were administered orally every day for 4 weeks, respectively. The protective effects of EPP and EPS on biochemical metabolic parameters, liver function, oxidative markers, activities of antioxidant enzymes and liver fibrosis were determined in CCl4-induced liver toxicity in rats. RESULTS: Compared with the sham group, the liver function was worse in CCl4 group, manifested as increased levels of serum alanine aminotransferase, aspartate aminotransferase and total bilirubin. EPP and EPS treatment significantly ameliorated these effects of CCl4. EPP and EPS attenuated CCl4-induced increase in the levels of TGF-ß1, hydroxyproline, hyaluronic acid laminin and procollagen type III. They also restored the decreased superoxide dismutase (SOD), glutathione peroxidase (GSH-Px) activities and inhibited the formation of lipid peroxidized products in rats treated with CCl4. CONCLUSION: The EPP and EPS have protective effects against liver fibrosis induced by CCl4, and its mechanisms might be associated with their antioxidant activity, the ability of decreasing the level of TGF-ß1 and inhibition of collagen synthesis.

[Correction of Fiber Throughput Variation Due to the Focal Ratio Degradation].

The focal ratio degradation (FRD) of optical fiber is one of major sources causing light loss in multi-fiber astronomical instruments. Meanwhile, the sky subtraction is crucial to multi-fiber spectra reduction, especially for the objects which are as faint as the sky background, not to mention for those even fainter ones. To improve the accuracy of sky subtraction, it is necessary to normalize the throughput among object fibers and sky sampling fibers. The rotation and twist during mounting and rotating could change the FRD of individual fibers, which means the variation of the transmission throughput among fibers. We investigate such throughput variation among LAMOST fibers and its correlation with the intensity of sky emission lines on all wavelength coverage in this paper. On the basis of this work, we present an approach to correcting the varied fiber throughput by measuring the intensity of the sky emission lines as the secondary throughput correction. This approach has been applied to LAMOST 2D Pipeline.

[Spectra Classification Based on Local Mean-Based K-Nearest Centroid Neighbor Method].

In the present paper, a local mean-based K-nearest centroid neighbor (LMKNCN) technique is used for the classification of stars, galaxies and quasars (QSOS). The main idea of LMKNCN is that it depends on the principle of the nearest centroid neighborhood(NCN), and selects K centroid neighbors of each class as training samples and then classifies a query pattern into the class with the distance of the local centroid mean vector to the samples . In this paper, KNN, KNCN and LMKNCN were experimentally compared with these three different kinds of spectra data which are from the United States SDSS-DR8. Among these three methods, the rate of correct classification of the LMKNCN algorithm is higher than the other two algorithms or comparable and the average rate of correct classification is higher than the other two algorithms, especially for the identification of quasars. Experiment shows that the results in this work have important significance for studying galaxies, stars and quasars spectra classification.

[Searching for Rare Celestial Objects Automatically from Stellar Spectra of the Sloan Digital Sky Survey Data Release Eight].

There are many valuable rare and unusual objects in spectra dataset of Sloan Digital Sky Survey (SDSS) Data Release eight (DR8), such as special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on, so it is extremely significant to search for rare and unusual celestial objects from massive spectra dataset. A novel algorithm based on Kernel dense estimation and K-nearest neighborhoods (KNN) has been presented, and applied to search for rare and unusual celestial objects from 546 383 stellar spectra of SDSS DR8. Their densities are estimated using Gaussian kernel density estimation, the top 5 000 spectra in descend order by their densities are selected as rare objects, and the top 300 000 spectra in ascend order by their densities are selected as normal objects. Then, KNN were used to classify the rest objects, and simultaneously K nearest neighbors of the 5 000 rare spectra are also selected as rare objects. As a result, there are totally 21 193 spectra selected as initial rare spectra, which include error spectra caused by deletion, redden, bad calibration, spectra consisting of different physically irrelevant components, planetary nebulas, QSOs, special white dwarfs (DZ, DQ, DC), carbon stars, white dwarf main-sequence binaries (WDMS), cataclysmic variable (CV) stars and so on. By cross identification with SIMBAD, NED, ADS and major literature, it is found that three DZ white dwarfs, one WDMS, two CVs with company of G-type star, three CVs candidates, six DC white dwarfs, one DC white dwarf candidate and one BL Lacertae (BL lac) candidate are our new findings. We also have found one special DA white dwarf with emission lines of Ca II triple and Mg I, and one unknown object whose spectrum looks like a late M star with emission lines and its image looks like a galaxy or nebula.

[SKLOF: a new algorithm to reduce the range of supernova candidates].

Supernova (SN) is called the "standard candles" in the cosmology, the probability of outbreak in the galaxy is very low and is a kind of special, rare astronomical objects. Only in a large number of galaxies, we have a chance to find the supernova. The supernova which is in the midst of explosion will illuminate the entire galaxy, so the spectra of galaxies we obtained have obvious features of supernova. But the number of supernova have been found is very small relative to the large number of astronomical objects. The time computation that search the supernova be the key to weather the follow-up observations, therefore it needs to look for an efficient method. The time complexity of the density-based outlier detecting algorithm (LOF) is not ideal, which effects its application in large datasets. Through the improvement of LOF algorithm, a new algorithm that reduces the searching range of supernova candidates in a flood of spectra of galaxies is introduced and named SKLOF. Firstly, the spectra datasets are pruned and we can get rid of most objects are impossible to be the outliers. Secondly, we use the improved LOF algorithm to calculate the local outlier factors (LOF) of the spectra datasets remained and all LOFs are arranged in descending order. Finally, we can get the smaller searching range of the supernova candidates for the subsequent identification. The experimental results show that the algorithm is very effective, not only improved in accuracy, but also reduce the operation time compared with LOF algorithm with the guarantee of the accuracy of detection.

[A New Distance Metric between Different Stellar Spectra: the Residual Distribution Distance].

Distance metric is an important issue for the spectroscopic survey data processing, which defines a calculation method of the distance between two different spectra. Based on this, the classification, clustering, parameter measurement and outlier data mining of spectral data can be carried out. Therefore, the distance measurement method has some effect on the performance of the classification, clustering, parameter measurement and outlier data mining. With the development of large-scale stellar spectral sky surveys, how to define more efficient distance metric on stellar spectra has become a very important issue in the spectral data processing. Based on this problem and fully considering of the characteristics and data features of the stellar spectra, a new distance measurement method of stellar spectra named Residual Distribution Distance is proposed. While using this method to measure the distance, the two spectra are firstly scaled and then the standard deviation of the residual is used the distance. Different from the traditional distance metric calculation methods of stellar spectra, when used to calculate the distance between stellar spectra, this method normalize the two spectra to the same scale, and then calculate the residual corresponding to the same wavelength, and the standard error of the residual spectrum is used as the distance measure. The distance measurement method can be used for stellar classification, clustering and stellar atmospheric physical parameters measurement and so on. This paper takes stellar subcategory classification as an example to test the distance measure method. The results show that the distance defined by the proposed method is more effective to describe the gap between different types of spectra in the classification than other methods, which can be well applied in other related applications. At the same time, this paper also studies the effect of the signal to noise ratio (SNR) on the performance of the proposed method. The result show that the distance is affected by the SNR. The smaller the signal-to-noise ratio is, the greater impact is on the distance; While SNR is larger than 10, the signal-to-noise ratio has little effect on the performance for the classification.

[Automatic Measurement of the Stellar Atmospheric Parameters Based Mass Estimation].

We have collected massive stellar spectral data in recent years, which leads to the research on the automatic measurement of stellar atmospheric physical parameters (effective temperature Teff, surface gravity log g and metallic abundance [Fe/ H]) become an important issue. To study the automatic measurement of these three parameters has important significance for some scientific problems, such as the evolution of the universe and so on. But the research of this problem is not very widely, some of the current methods are not able to estimate the values of the stellar atmospheric physical parameters completely and accurately. So in this paper, an automatic method to predict stellar atmospheric parameters based on mass estimation was presented, which can achieve the prediction of stellar effective temperature Teff, surface gravity log g and metallic abundance [Fe/H]. This method has small amount of computation and fast training speed. The main idea of this method is that firstly it need us to build some mass distributions, secondly the original spectral data was mapped into the mass space and then to predict the stellar parameter with the support vector regression (SVR) in the mass space. we choose the stellar spectral data from the United States SDSS-DR8 for the training and testing. We also compared the predicted results of this method with the SSPP and achieve higher accuracy. The predicted results are more stable and the experimental results show that the method is feasible and can predict the stellar atmospheric physical parameters effectively.

[The radial velocity measurement accuracy of different spectral type low resolution stellar spectra at different signal-to-noise ratio].

The radial velocity of the star is very important for the study of the dynamics structure and chemistry evolution of the Milky Way, is also an useful tool for looking for variable or special objects. In the present work, we focus on calculating the radial velocity of different spectral types of low-resolution stellar spectra by adopting a template matching method, so as to provide effective and reliable reference to the different aspects of scientific research We choose high signal-to-noise ratio (SNR) spectra of different spectral type stellar from the Sloan Digital Sky Survey (SDSS), and add different noise to simulate the stellar spectra with different SNR. Then we obtain theradial velocity measurement accuracy of different spectral type stellar spectra at different SNR by employing a template matching method. Meanwhile, the radial velocity measurement accuracy of white dwarf stars is analyzed as well. We concluded that the accuracy of radial velocity measurements of early-type stars is much higher than late-type ones. For example, the 1-sigma standard error of radial velocity measurements of A-type stars is 5-8 times as large as K-type and M-type stars. We discuss the reason and suggest that the very narrow lines of late-type stars ensure the accuracy of measurement of radial velocities, while the early-type stars with very wide Balmer lines, such as A-type stars, become sensitive to noise and obtain low accuracy of radial velocities. For the spectra of white dwarfs stars, the standard error of radial velocity measurement could be over 50 km x s(-1) because of their extremely wide Balmer lines. The above conclusion will provide a good reference for stellar scientific study.