RESUMO
The LAMOST project, the world's largest sky survey project being implemented in China, urgently needs an automatic stars recognition and classification system. This paper presents a method for auto-recognizing the stars based on spectral feature. This method consists of three main steps: First, the integral information of spectral lines is calculated and the stellar Balmer lines are detected by using the wavelet features of spectral lines. Then, the characteristic frequency of M-type stars and the locations of absorption bands are obtained accurately through the wavelet features of absorption bands. Finally, based on the results of the former step, the emission-line stars, M-type stars and early-type stars can be recognized. The extensive experiments with real observed spectra from the SDSS DR4 show that the method can robustly recognize stellar spectra, the correct rate of the emission-line stars is as high as 97.5%, the correct rate of M-type stars is as high as 98.1% and the correct rate of early-type stars is as high as 96.8%. The error rate of the quasars and the galaxies is less than 2%. This method is designed to automatically recognize stellar spectra with relative flux and low signal-to-noise ratio, which is applicable to the LAMOST data.
RESUMO
The LAMOST project, the world's largest sky survey project being implemented in China, urgently needs an automatic stars recognition system. The present paper presents a method for automatic recognition of stellar spectra based on feature matching of spectral lines. This method consists of three main steps: First, the features of spectral lines! in the observed spectra are extracted using the wavelet transform. Then, the correlations between the extracted features and the feature templates of the stellar spectral lines are computed. Finally, based on the results of the former step, the stellar spectra can be recognized. The extensive experiments with real observed spectra from the SDSS DR4 show that the method can robustly recognize stellar spectra, and the correct rate of this method is as high as 96.7%. This method is designed to automatically recognize stellar spectra with relative flux and low signal-to-noise ratio, which is applicable to the LAMOST data and helps in the structure study of stars and galaxy etc.
RESUMO
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. The rationality/correctness of the model, and the satisfactory performance of the proposed methods have been obtained by the experiments over normal galaxies (NGs) and quasi-stellar object (Qso). Furthermore, the authors theoretically analyze, compare and evaluate them. In particular, this work indicated that the conventional method is worse than the proposed one. And the investigation is also particularly significant for other automatic spectrum processing study, e. g. redshift determination, effective temperature, metallic estimation, etc.
RESUMO
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. Then the redshifts of quasar spectra can be estimated by using the multi-scaling feature matching. The experiment with the 15, 715 quasars from the SDSS DR2 shows that the correct rate of redshift estimated by the method is 95.13% within an error range of 0. 02. This method was designed to obtain the redshifts of quasar spectra with relative flux and a low signal-to-noise ratio, which is applicable to the LAMOST data and helps to study quasars and the large-scale structure of the universe etc.
RESUMO
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. Then, the noise with coefficients of the 4th scale is eliminated by the local correlation algorithm and threshold in the wavelet packet domain. After that, middle and high frequency coefficients are selected to reconstruct the feature of the spectral lines. Finally, with the reconstructed feature of the spectral lines, spectral lines in observed spectra are searched. The results of our experiments, which include the spectral lines of stars, normal galaxies and active galaxies, show that the method can robustly and accurately extract the spectral lines. The method was applied to extract the SDSS spectral lines and compute the redshifts with those lines. By comparing the redshifts with those given by SDSS, the extraction has proven successful and practical.
RESUMO
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. Here, by "late-type stars" we mean those stars with strong molecule absorption bands, including oxygen-rich M, L and T type stars and carbon-rich C stars. Based on experimental results, the authors find that after a wavelet transform with 5 scales on the late-type stars spectra, their frequency spectrum of the transformed coefficient on the 5th scale consistently manifests a unimodal distribution, and the energy of frequency spectrum is largely concentrated on a small neighborhood centered around the unique peak. However, for the spectra of other celestial bodies, the corresponding frequency spectrum is of multimodal and the energy of frequency spectrum is dispersible. Based on such a finding, the authors presented a wavelet-transform-based automatic late-type stars detection method. The proposed method is shown by extensive experiments to be practical and of good robustness.
