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.

[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.

[The backgroud sky subtraction around [OIII] line in LAMOST QSO spectra].

At present, most sky-subtraction methods focus on the full spectrum, not the particular location, especially for the backgroud sky around [OIII] line which is very important to low redshift quasars. A new method to precisely subtract sky lines in local region is proposed in the present paper, which sloves the problem that the width of Hß-[OIII] line is effected by the backgroud sky subtraction. The exprimental results show that, for different redshift quasars, the spectral quality has been significantly improved using our method relative to the original batch program by LAMOST. It provides a complementary solution for the small part of LAMOST spectra which are not well handled by LAMOST 2D pipeline. Meanwhile, This method has been used in searching for candidates of double-peaked Active Galactic Nuclei.

[The study of M dwarf spectral classification].

As the most common stars in the galaxy, M dwarfs can be used to trace the structure and evolution of the Milky Way. Besides, investigating M dwarfs is important for searching for habitability of extrasolar planets orbiting M dwarfs. Spectral classification of M dwarfs is a fundamental work. The authors used DR7 M dwarf sample of SLOAN to extract important features from the range of 600-900 nm by random forest method. Compared to the features used in Hammer Code, the authors added three new indices. Our test showed that the improved Hammer with new indices is more accurate. Our method has been applied to classify M dwarf spectra of LAMOST.

[Stellar spectrum parameter measurement based on line index by linear regression].

Using the Lick line index, according to the magnanimity characteristics of the spectrum an efficient algorithm of the atmospheric physical parameters measurement by the linear regression method from the point of view of statistical regression was designed. The linear regression was used to achieve the best regression effect by selecting the type of regression and the composition of line index. The formula obtained from the regression model makes the computation speed fast when applied to new data, and the clarity and ease of analysis processing can not be reached by other methods. The experimental results show that through the line index regression method to get the atmospheric physical parameters is feasible.

[Comparison of different template matching methods of automatic stellar spectrum parameter measurement].

Template matching is one of the most commonly used methods of automatic stellar spectrum parameter measurement The present paper made comparisons among the three commonly used template matching methods K-Nearest Neighbor (KNN), the Chi-square minimization and cross correlation method. The Continuum normalization and flux normalization were made first. Then the three mentioned methods were compared in measurement results of stellar spectrum parameters. Experiments on SDSS DR8 large sample spectra showed that the cross correlation method had comparable advantages.

[An automated method to fit stellar continuum based on statistic windows].

A novel statistic window based method to fit stellar continuum is proposed. First a stellar spectrum is divided into a series of statistic windows in which a certain percent of flux points is selected according to S/N ratio; then low order polynomial iteration fitting is carried out based on the selected flux points to obtain the stellar continuum. Experimental results show that the continuum obtained by the proposed method is more close to the real continuum, compared to other existed methods. This method has a better practical applicability and robustness to all kinds of spectra (except M-type spectrum) in SDSS. It also works well for Guoshoujing Telescope (LAMOST) pilot survey spectra.

[Effects of spectral resolution and noise on the measurement of indices].

In the present paper, we analysed the effects of spectral resolutions and signal-to-noise ratios (SNRs) on 19 atomic absorption line indices of Lick index system. First of all, adopting method of convolving a spectrum with a Gaussian profile, we transformed spectra into those under different resolutions and then measured the line indices on them. Comparisons of the indices under various resolutions allow to investigate the impact of spectral resolution change on the accuracy of measurements of indices. Secondly, by adding random noises with different Gaussian distribution to a spectrum, the authors transformed theoretical spectra with no noises into those under diverse SNRs and then measured line indices on them. Comparisons of the indices under different SNRs greatly helped analyse the influence of SNR on the precision of the measurements of line indices. It comes from comparisons and analysis that the spectral resolution change can cause an index measurement change depending on the extent of the change of spectral resolution. Such a kind of change relationship varies with the indices. The lower the SNR, the less precise the measurements of indices. The effect of SNR on the measurements of indices can be ignored if SNR is larger than 25.

[A new automated method to identify emission line star from massive spectra].

Stellar spectra are characterized by obvious absorption lines or absorption bands, while those with emission lines are usually special stars such as cataclysmic variable stars (CVs), HerbigAe/Be etc. The further study of this kind of spectra is meaningful. The present paper proposed a new method to identify emission line stars (ELS) spectra automatically. After the continuum normalization is done for the original spectral flux, line detection is made by comparing the normalized flux with the mean and standard deviation of the flux in its neighbor region The results of the experiment on massive spectra from SDSS DR8 indicate that the method can identify ELS spectra completely and accurately. Since no complex transformation and computation are involved in this method, the identifying process is fast and it is ideal for the ELS detection in large sky survey projects like LAMOST and SDSS.