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Fu-qing Duan

Publications and source records attributed to Fu-qing Duan.

3 recordsLinked to original sources

[A method for redshift determination of quasars based on cross correlation].

This paper presents a novel method for redshift determination of quasars. Firstly, a group of redshifts were determined using the emission line info extracted from the observed spectrum; Secondly, the template was redshifted according to the candidates, and the correlation between the observed spectrum and the redshifted template was measured. Finally, the redshift candidate corresponding to the highest correlation was chosen as the redshift. Compared with the existing methods based on spectral line matching, the proposed method has a lower dependence on the quality of spectral line extraction. Experiments show that this method is robust and superior to the methods based on spectral linematching.

Algorithms↗

[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.

English Abstract↗

[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. Finally, the authors findthe point of maximum density within the data set, then the average of the data in epsilon-neighbor of the point is regarded as the redshift estimation. The information of feature wavelength and spectral line type is used in this method so that it can deal with every kind of spectra. Experiments show that our method is stable and the correct identification rate is high.

English Abstract↗