The double-peaked profiles in spectral data are very rare and valuable for the astronomers. Their recognitions are largely depended on visually inspect. The main reasons for auto-search of such spectra are the complex and distinct characteristics of astronomical data. In this paper, we address the problems by a double-peaked profiles search algorithm (called DoPS) based on relevant subspace (RS) and support vector machine (SVM). First, characteristics subspace is extracted by using the relevant subspace mining algorithm, in which the local density factor λ is particularly defined to measure the data sparsity. The characteristics of double-peaked profiles are represented by using the locations, the interval spaces, and strength ratio of double peaks. Second, the characteristics set is analyzed and grouped into three subsets according to the correlations among the characteristics based on the frequent patterns and rough set theory. Third, the doublepeaked profiles search algorithm is proposed by using the support vectors trained from the labeled samples as thresholds. Finally, several spectral data sets from the LAMOST survey are employed to test the DoPS. The experimental results indicate that DoPS presents high performance than other similar algorithms in terms of time efficiency, noise immunity and recall, and reduced rates. INDEX TERMS DoPS, characteristics extraction, support vector machine, double-peaked profiles.
The LAMOST II survey began in the fall of 2018, and LAMOST formally released the obtained medium-resolution spectra (MRS) as well as catalogs of stellar parameters and radial velocities (RV) derived from the MRS in the seventh LAMOST data release (DR7). Compared with the RVs and parameters from high-resolution SDSS14/APOGEE spectra, nearly no RV discrepancy was found, with only dispersion around one km s−1. The T eff of MRS was 27.79 K systematically lower than that of APOGEE, and the 1σ difference was approximately 60.40 K. For metallicity, the [Fe/H] of the MRS was 0.11 dex poorer than that of APOGEE, with a dispersion of 0.07 dex. More apparently, the offset of log g was 0.14 dex, and the scatter was 0.23 dex. The gravities of APOGEE have been corrected through seismological data. Similarly, in this study, we tried to calibrate LAMOST MRS log g with the Kepler data for red clump stars and red giant branch stars based on two fitted calibration relations. In addition, we found log g of a small fraction late-K type giants mistakenly measured as dwarfs, and vice versa, on the HR diagram generated with LAMOST T eff and Gaia absolute magnitudes. This was because the MRS parameters were measured from blue band spectra only. The measurements of log g for late-K stars can be significantly improved by adding red spectra.
In big data era, the special data with rare characteristics may be of great significations. However, it is very difficult to automatically search these samples from the massive and high-dimensional datasets and systematically evaluate them. The DoPS, our previous work [1], provided a search method of rare spectra with double-peaked profiles from massive and high-dimensional data of LAMOST survey. The identification of the results is mainly depended on visually inspection by astronomers. In this paper, as a follow-up study, a new lattice structure named SVM-Lattice is designed based on SVM(Support Vector Machine) and FCL(Formal Concept Lattice) and particularly applied in the recognition and evaluation of rare spectra with double-peaked profiles. First, each node in the SVM-Lattice structure contains two components: the intents are defined by the support vectors trained by the spectral samples with the specific characteristics, and the relevant extents are all the positive samples classified by the support vectors. The hyperplanes can be extracted from every lattice node and used as classifiers to search targets by categories. A generalization and specialization relationship is expressed between the layers, and higher layers indicate higher confidence of targets. Then, including a SVM-Lattice building algorithm, a pruning algorithm based on association rules, and an evaluation algorithm, the supporting algorithms are provided and analysed. Finally, for the recognition and evaluation of spectra with double-peaked profiles, several data sets from LAMOST survey are used as experimental dataset. The results exhibit good consistency with traditional methods, more detailed and accurate evaluations of classification results, and higher searching efficiency than other similar methods. INDEX TERMS SVM-Lattice, double-peaked profiles, support vector machine, formal concept lattice.
The Hα emission line in rest wavelength frame of optical spectra is valuable characteristics for nebulae detection. Searching and recognizing the spectra with Hα emission line from massive data are necessary for the further study, while the most of methods existed currently do not adapt to such spectral data, especially for the spectra with weak Hα emission line. To address this issue, a new algorithm (named WEDA) for detection of spectra with Hα emission line is provided in this paper. Firstly, the difference factor µ between the line characteristics of the specific data is defined as its weight in recognizing of the whole lines table. Secondly, a tuning function f (τ, δ) based on the momentum formula is defined to update the weights during the process. In this step, the spectra with Hα emission line are analysed and classified as 3 different situations. The amount of spectra with Hα emission line is different in 3 different situations, so the speed of weight of update is different in 3 different situations. The weight of update helps us detect the data containing weak Hα emission line in the 3 situations. Based on this, a new integrated algorithm especially for the detection of the spectra with Hα is provided. In the end, by using several spectral datasets from the DR5 of LAMOST survey, experiments results indicate that the WEDA shows higher accuracy basically unaffected by the dataset size and the signal to noise ratio(SNR) than the other similar algorithms.
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