RESUMEN
In this paper, a new method based on LASSO algorithm is studied for the estimation of stellar alpha element abundance. The information of alpha elements (O, Mg, Ca, Si, and Ti) of massive stars will help us to better understand the evolution of the galaxy. Presently the main method of determining the alpha element abundances from the low resolution spectra is the template matching method. However, it is difficult for us to optimize the algorithm parameters and the algorithm is sensitive to the noise. Thus, it is necessary to study the new method to determine the abundance. The experimental results show that the accuracy of LASSO algorithm on ELODIE spectra is 0.003 (0.078) dex. To explore the impact of the spectral resolution variation, we use ELODIE spectra to generate the spectral data sets with following resolutions: 42 000, 21 000, 10 500, 4 200 and 2 100 by using the Gaussian convolution. The results of the LASSO algorithm on these data sets are 0.003 3 (0.078) dex, -0.05 (0.059) dex, -0.007 (0.069) dex and -0.004 5 (0.067) dex, respectively. These results show that the LASSO algorithm is not sensitive to the change of the resolution. In order to verify the robustness of LASSO algorithm against the change of SNRs, we use ELODIE to generate the spectral data sets with following SNRs: 30, 25, 20, 15 and 5. The results of LASSO algorithm on the above data sets are: -0.002 (0.076) dex, -0.090 (0.073) dex, 0.003 6 (0.075) dex, 0.007 6 (0.078) dex and -0.009 (0.080) dex, respectively. Thus, LASSO algorithm is not sensitive to the change of SNR. Therefore, the LASSO algorithm is suitable for low resolution and low SNR spectra such as LAMOST and SDSS spectra. The accuracy of Lasso algorithm on the SDSS spectra is 0.003 7 (0.097) dex, and the results of LASSO on globular and open clusters show good agreement with literature values (within 1σ). Therefore, the LASSO algorithm can be used to estimate the alpha element abundances of stars.
RESUMEN
How to find the spectra misclassified by traditional methods is the key problem that has been widely studied by the experts of astronomical data processing. We found that Isomap algorithm performs well for this problem. By comparing the performance of Isomap with that of principal component analysis (PCA), we found that (1) Isomap can project the spectra with similar features together and project the spectra with different features far away, while PCA may project the spectra with different features into nearby regions; (2) the outliers given by Isomap can be easily determined, and most of the outliers are binary stars with high scientific values; while the outliers given by PCA are difficult to determine and most of outliers are not binary stars. Thus, Isomap is more efficient than PCA in finding the outliers. Since the spectral data used in experiment are the spectra from the ninth data release of Sloan Digital Sky Survey (SDSS DR9), Isomap can find the spectra misclassified by SDSS pipeline efficiently and improve the classification accuracy obviously. Furthermore, since most of the spectra misclassified by SDSS pipeline are binary stars, Isomap can improve the efficiency of finding the binary stars with high scientific values. Though the experiment results show that Isomap is more sensitive to the noise than PCA, this disadvantage will not affect the application of Isomap in spectral classification since most of the spectra with low signal-to-noise ratios are the spectra whose spectral type can't be determined manually.