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The LAMOST-textit{K}2 (Ltextit{K}2) project, initiated in 2015, aims to collect low-resolution spectra of targets in the textit{K}2 campaigns, similar to LAMOST-textit{Kepler} project. By the end of 2018, a total of 126 Ltextit{K}2 plates had been observed by LAMOST. After cross-matching the catalog of the LAMOST data release 6 (DR6) with that of the textit{K}2 approved targets, we found 160,619 usable spectra of 84,012 objects, most of which had been observed more than once. The effective temperature, surface gravity, metallicity, and radial velocity from 129,974 spectra for 70,895 objects are derived through the LAMOST Stellar Parameter Pipeline (LASP). The internal uncertainties were estimated to be 81 K, 0.15 dex, 0.09 dex and 5 kms$^{-1}$, respectively, when derived from a spectrum with a signal-to-noise ratio in the $g$ band (SNR$_g$) of 10. These estimates are based on results for targets with multiple visits. The external accuracies were assessed by comparing the parameters of targets in common with the APOGEE and GAIA surveys, for which we generally found linear relationships. A final calibration is provided, combining external and internal uncertainties for giants and dwarfs, separately. We foresee that these spectroscopic data will be used widely in different research fields, especially in combination with textit{K}2 photometry.
We set out to determine stellar labels from low-resolution survey spectra of hot, OBA stars with effective temperature (Teff) higher than 7500K. This fills a gap in the scientific analysis of large spectroscopic stellar surveys such as LAMOST, which
The nearly continuous light curves with micromagnitude precision provided by the space mission Kepler are revolutionising our view of pulsating stars. They have revealed a vast sea of low-amplitude pulsation modes that were undetectable from Earth. T
LAMOST DR5 released more than 200,000 low resolution spectra of early-type stars with S/N>50. Searching for metallic-line (Am) stars in such a large database and study of their statistical properties are presented in this paper. Six machine learning
182 single-lined hot subdwarf stars are identified by using spectra from the sixth and seventh data release (DR6 and DR7) of the Large Sky Area Multi-Object Fibre Spectroscopic Telescope (LAMOST) survey. We classified all the hot subdwarf stars using
LAMOST Data Release 5, covering $sim$17,000 $deg^2$ from $-10^{circ}$ to $80^{circ}$ in declination, contains 9 millions co-added low resolution spectra of celestial objects, each spectrum combined from repeat exposure of two to tens of times during