A new set of gratings with medium resolution (R ∼ 7500) has been mounted on the LAMOST spectrographs, and the wavelength windows range in 490 ∼ 540nm and 640 ∼ 690 nm respectively for blue and red spectrograph arm. Commissioning observation has been conducted to test the survey based on 16 spectrographs and 4000 fibers. Meanwhile, a spectral analysis pipeline has been developing to get more precise stellar parameters, radial velocities and abundance of chemical elements. Instrument profiles are calculated for each fiber at each exposure according to emission lines both from arc lamp. A template grid spectra with R ∼ 7500 for fundamental parameter (Teff, logg, and [Fe/H] ) are selected from Elodie. During the commissioning observation, each star have been visited for several times, and a fraction targets include APOGEE, Kepler and PASTEL objects which have high precisely measured parameters. With the commissioning spectra, we can understand instrument performance, intrinsic precision of repeat observations, and the accuracy of the pipeline.
We adopt a machine learning (ML) method, introduced in detail in our previous work, to mine spectroscopicallyconfirmed DB white dwarf (DBWD) from LAMOST Data Release(DR) 5. The unique features of DBWD are extracted from between known DB spectra and all other released data. We take advantage of these DBWD samples and features by classifying a certain amount of LAMOST spectral data by LAMOST 1D Pipeline. At first, two groups of clustering centers are produced as DBWD templates using k-means. Then, we build four control groups, whether to consider feature location and which clustering centers are employed, to conduct classification tests. The experiment demonstrates that taking particular features as weights of spectral data could improve classification accuracy.
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