[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 Hβ-[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.

[Galaxy spectrum subtraction of a mixed spectrum based on two class PCA eigen-spectra].

The authors present a new method called two class PCA for decomposing the mixed spectra, namely, for subtracting the host galaxy contamination from each SN spectrum. The authors improved the quality of reconstructed galaxy spectrum and computational efficiency, and these improvements were realized because we used both the PCA eigen spectra of galaxy templates library and SN templates library to model the mixed spectrum. The method includes mainly three steps described as follows.

[Reducing the searching range of supernova candidates automatically in a flood of spectra of galaxies].

Supernova (SN) is one of the most intense astronomical phenomena among the known stellar activities, but compared with several billion astronomical objects which people have probed, the number of supernova the authors have observed is very small. Therefore, the authors need to find faster and higher-efficiency approaches to searching supernova. In the present paper, we present a novel automated method, which can be successfully used to reduce the range of searching for 1a supernova candidates in a huge number of galaxy spectra.

[Kernel regression application in estimating stellar fundamental parameters].

The three fundamental parameters of stellar atmosphere, i.e. the effective temperature, the surface gravity, and the metallic, determine the continuum and spectral lines in the stellar spectrum.

[The application of adaptive boosting method in automated spectral classification of active galactic nuclei].

Given a set of low-redshift spectra of active galactic nuclei, the wave bands of spectra in the rest frame were intercepted according to the different features of emission lines of broad-line AGNs and narrow-line AGNs, and an adaptive boosting (Adaboost) method was developed to carry out the classification experiments of feature fusion. As a result, the wave band of Halpha and [N II] was confirmed to be the main discriminative feature between broad-line AGNs and narrow-line AGNs. Then based on the wave band of Halpha and [N II], the Adaboost method was used for the spectral classification.

[Celestial spectrum flux standardization for classification].

Celestial spectra should be preprocessed before automated classification to eliminate the disturbance of noise, observa-tion environment, and flux aberrance. In the present work, the authors studied the spectrum flux standardization problem. By analyzing the disturbing factors and their characteristics, the authors put forward a theoretical model for spectra flux, and corre-spondingly give several flux standardizing methods.

[Studies of spectra classification based on kernel covering algorithm].

A kernel based covering algorithm, called the kernel covering algorithm (KCA), is proposed for the classification of celestial spectra. This algorithm is a combination of kernel trick with the covering algorithm, and is used to extract the support vectors in feature space. The experiments show that the classification result based on KCA is a little less than that based on SVM.

[Spectra classification based on generalized discriminant analysis].

A kernel based generalized discriminant analysis (GDA) technique is proposed for the classification of stars, galaxies, and quasars. GDA combines the LDA algorithm with kernel trick, and samples are projected by nonlinear mapping onto the feature space F with high dimensions, and then LDA is conducted in F. Also, it could be inferred that GDA which combines the extension of Fisher's criterion with kernel trick is complementary to kernel Fisher discriminant framework.

[A method for obtaining redshifts of quasars based on wavelet multi-scaling feature matching].

The LAMOST project, the world's largest sky survey project being implemented in China, is expected to obtain 10(5) quasar spectra. The main objective of the present article is to explore methods that can be used to estimate the redshifts of quasar spectra from LAMOST. Firstly, the features of the broad emission lines are extracted from the quasar spectra to overcome the disadvantage of low signal-to-noise ratio.

[A novel method for the determination of redshifts of normal galaxies by non-linear dimensionality reduction].

It is difficult to determine the redshifts of normal galaxies (NG) from their spectra because of their common weak absorption property. In the present work, a novel method is proposed to effectively deal with this issue. The proposed method is composed of the following three parts: At first, the wavelet transform coefficients at the fourth scaling are experimentally found to be appropriate and used as our features to represent the absorption information from NG absorption lines, break points, and absorption bands.

[Automated recognition of quasars based on adaptive radial basis function neural networks].

Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy. This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks (RBFN). The proposed method is composed of the following three parts: (1) The feature space is reduced by the PCA (the principal component analysis) on the normalized input spectra; (2) An adaptive RBFN is constructed and trained in this reduced space.

[A method for auto-extraction of spectral lines based on convolution type of wavelet packet transformation].

The important astrophysical information is hidden in spectral lines of astronomical spectra. The presen paper presents a method for auto-extraction of spectral lines based on convolution type of wavelet packet. This method consists of four main steps: First, the observed spectra are transformed by convolution type of wavelet packet with 4th scale.

[Non-parameter estimation algorithm to determine stellar effective temperature].

The effective temperature of a star is one of the most important parameters, which determine the continuum and spectral lines in the stellar spectrum. A non-parameter estimation algorithm is proposed to estimate the stellar effective temperature in the present paper. Firstly, the spectrum data is processed by principal component analysis(PCA), then, an estimating model based on a Gaussian kernel function is set up using the PCA data and their temperatures.

[Density estimation based model matching method for redshift determination].

The present paper proposes a model matching method based on density estimation for redshift determination, in whichthe problem of redshift determination is translated into the problem of searching for the point of maximum density within a data set. At first, the mean shift-based method for auto-extraction of spectral lines is used to get feature spectrallines. Secondly, according tothe redshift formula, the authors use the feature wavelength array and the spectral template to get a data set.

[A novel spectral classifier based on coherence measure].

Classification and discovery of new types of celestial bodies from voluminous celestial spectra are two important issues in astronomy, and these two issues are treated separately in the literature to our knowledge. In the present paper, a novel coherence measure is introduced which can effectively measure the coherence of a new spectrum of unknown type with the training sampleslocated within its neighbourhood, then a novel classifier is designed based on this coherence measure. The proposed classifier is capable of carrying out spectral classification and knowledge discovery simultaneously.

[Mean shift based auto-extraction of spectral lines for non-emission-line objects].

The mean shift algorithm is used. At first, the property that mean shift vectors always point toward local maxima of the density is used to get the pseudo continuum; secondly, mean shift filtering is a goodedge preserving smoothing, which canadaptively reduce the amount of smoothing near feature spectral lines, so the authors use mean shift filtering in noise reduction after the noramalization of continuum spectra; finally, the authors extract feature spectral lines by setting local thresholds. The experiments on both stars and normal galaxies show that our method can extract spectral lines accurately, which is helpful to the parameter measure and the automatic classification of spectra based on spectral lines.

[A wavelet-transform-based method for the automatic detection of late-type stars].

The LAMOST project, the world largest sky survey project, urgently needs an automatic late-type stars detection system. However, to our knowledge, no effective methods for automatic late-type stars detection have been reported in the literature up to now. The present study work is intended to explore possible ways to deal with this issue.

[Using neural networks based template matching method to obtain redshifts of normal galaxies].

Galaxies can be divided into two classes: normal galaxy (NG) and active galaxy (AG). In order to determine NG redshifts, an automatic effective method is proposed in this paper, which consists of the following three main steps: (1) From the template of normal galaxy, the two sets of samples are simulated, one with the redshift of 0.0-0.