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Register a new userMetallic-Line Stars Identified from Low Resolution Spectra of LAMOST DR5
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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 algorithms were experimented with using known Am spectra, and both the empirical criteria method(Hou et al. 2015) and the MKCLASS package(Gray et al. 2016) were also investigated. Comparing their performance, the random forest (RF) algorithm won, not only because RF has high successful rate but also it can derives and ranks features. Then the RF was applied to the early type stars of DR5, and 15,269 Am candidates were picked out. Manual identification was conducted based on the spectral features derived from the RF algorithm and verified by experts. After manual identification, 9,372 Am stars and 1,131 Ap candidates were compiled into a catalog. Statistical studies were conducted including temperature distribution, space distribution, and infrared photometry. The spectral types of Am stars are mainly between F0 and A4 with a peak around A7, which is similar to previous works. With the Gaia distances, we calculated the vertical height Z from the Galactic plane for each Am star. The distribution of Z suggests that the incidence rate of Am stars shows a descending gradient with increasing jZj. On the other hand, Am stars do not show a noteworthy pattern in the infrared band. As wavelength gets longer, the infrared excess of Am stars decreases, until little or no excess in W1 and W2 bands.
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We present the determination of stellar parameters and individual elemental abundances for 6 million stars from $sim$8 million low-resolution ($Rsim1800$) spectra from LAMOST DR5. This is based on a modeling approach that we dub $The$ $Data$--$Driven$ $Payne$ ($DD$--$Payne$), which inherits essential ingredients from both {it The Payne} citep{Ting2019} and $The$ $Cannon$ citep{Ness2015}. It is a data-driven model that incorporates constraints from theoretical spectral models to ensure the derived abundance estimates are physically sensible. Stars in LAMOST DR5 that are in common with either GALAH DR2 or APOGEE DR14 are used to train a model that delivers stellar parameters ($T_{rm eff}$, $log g$, $V_{rm mic}$) and abundances for 16 elements (C, N, O, Na, Mg, Al, Si, Ca, Ti, Cr, Mn, Fe, Co, Ni, Cu, and Ba) when applied to LAMOST spectra. Cross-validation and repeat observations suggest that, for ${rm S/N}_{rm pix}ge 50$, the typical internal abundance precision is 0.03--0.1,dex for the majority of these elements, with 0.2--0.3,dex for Cu and Ba, and the internal precision of $T_{rm eff}$ and $log g$ is better than 30,K and 0.07,dex, respectively. Abundance systematics at the $sim$0.1,dex level are present in these estimates, but are inherited from the high-resolution surveys training labels. For some elements, GALAH provides more robust training labels, for others, APOGEE. We provide flags to guide the quality of the label determination and to identify binary/multiple stars in LAMOST DR5. The abundance catalogs are publicly accessible via href{url}{http://dr5.lamost.org/doc/vac}.
The LAMOST-textit{K}2 (Ltextit{K}2) project, initiated in 2015, aims to collect low-resolution spectra of targets in the textit{K}2 campaigns, similar to LAMOST-textit{Kepler} project. By the end of 2018, a total of 126 Ltextit{K}2 plates had been observed by LAMOST. After cross-matching the catalog of the LAMOST data release 6 (DR6) with that of the textit{K}2 approved targets, we found 160,619 usable spectra of 84,012 objects, most of which had been observed more than once. The effective temperature, surface gravity, metallicity, and radial velocity from 129,974 spectra for 70,895 objects are derived through the LAMOST Stellar Parameter Pipeline (LASP). The internal uncertainties were estimated to be 81 K, 0.15 dex, 0.09 dex and 5 kms$^{-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.
In this work, we present a catalog of 2651 carbon stars from the fourth Data Release (DR4) of the Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST). Using an efficient machine-learning algorithm, we find out these stars from more than seven million spectra. As a by-product, 17 carbon-enhanced metal-poor (CEMP) turnoff star candidates are also reported in this paper, and they are preliminarily identified by their atmospheric parameters. Except for 176 stars that could not be given spectral types, we classify the other 2475 carbon stars into five subtypes including 864 C-H, 226 C-R, 400 C-J, 266 C-N, and 719 barium stars based on a series of spectral features. Furthermore, we divide the C-J stars into three subtypes of CJ( H), C-J(R), C-J(N), and about 90% of them are cool N-type stars as expected from previous literature. Beside spectroscopic classification, we also match these carbon stars to multiple broadband photometries. Using ultraviolet photometry data, we find that 25 carbon stars have FUV detections and they are likely to be in binary systems with compact white dwarf companions.
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 offers spectra for millions of stars at R=1800. We first explore the theoretical information content of such spectra for determining stellar labels, via the Cramer-Rao bound. We show that in the limit of perfect model spectra and observed spectra with S/N of 100, precise estimates are possible for a wide range of stellar labels: not only the effective temperature Teff, surface gravity logg, and projected rotation velocity vsini, but also the micro-turbulence velocity, Helium abundance and the elemental abundances [C/H], [N/H], [O/H], [Si/H], [S/H], and [Fe/H]. Our analysis illustrates that the temperature regime of around 9500K is challenging, as the dominant Balmer and Paschen line strength vary little with Teff. We implement the simultaneous fitting of these 11 stellar labels to LAMOST hot-star spectra using the Payne approach, drawing on Kuruczs ATLAS12/SYNTHE LTE spectra as the underlying models. We then obtain stellar parameter estimates for a sample of about 330,000 hot stars with LAMOST spectra, an increase by about two orders of magnitude in sample size. Among them, about 260,000 have good Gaia parallaxes (S/N>5), and more than 95 percent of them are luminous stars, mostly on the main sequence; the rest reflects lower luminosity evolved stars, such as hot subdwarfs and white dwarfs. We show that the fidelity of the abundance estimates is limited by the systematics of the underlying models, as they do not account for NLTE effects. Finally, we show the detailed distribution of vsini of stars with 8000-15,000K, illustrating that it extends to a sharp cut-off at the critical rotation velocity, across a wide range of temperatures.
Combing Gaia DR2 with LAMOST DR5, we spectroscopically identified 924 hot subdwarf stars, among which 32 stars exhibit strong double-lined composite spectra. We measured the effective temperature $T_{rm eff}$, surface gravity $log,g$, helium abundance $y=n{rm He}/n{rm H}$, and radial velocities of 892 non-composite spectra hot subdwarf stars by fitting LAMOST observations with Tlusty/Synspec non-LTE synthetic spectra. We outlined four different groups in the $T_{rm eff}-log,g$ diagram with our helium abundance classification scheme and two nearly parallel sequences in the $T_{rm eff}-log(y)$ diagram. 3D Galactic space motions and orbits of 747 hot subdwarf stars with $(G_{BP}-G_{RP})_{0}<-0.36$ mag were computed using LAMOST radial velocities and Gaia parallaxes and proper motions. Based on the $U-V$ velocity diagram, $J_{z}-$eccentricity diagram, and Galactic orbits, we derived Galactic population classifications and the fractional distributions of the four hot subdwarf helium groups in the halo, thin disk and thick disk. Comparisons with the predictions of binary population synthesis calculations (Han 2008) suggest that He-rich hot subdwarf stars with $log(y)ge0$ are from the double helium white dwarfs merger, He-deficient hot subdwarf stars with $-2.2lelog(y)<-1$ from the common envelope ejection, and He-deficient hot subdwarf stars with $log(y)<-2.2$ from the stable Roche lobe overflow channels. The relative number of He-rich hot subdwarf stars with $-1lelog(y)<0$ and $log(y)ge0$ in the halo is more than twice the prediction of Zhang et al.(2017), even more than six times in the thin disk, which implies that the mergers of helium white dwarfs with low mass main sequence stars may not be the main formation channel of He-rich hot subdwarf stars with $-1lelog(y)<0$, specially in younger environments.
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