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Register a new userData-driven spectroscopic estimates of absolute magnitude, distance and binarity -- method and catalog of 16,002 O- and B-type stars from LAMOST
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We present a data-driven method to estimate absolute magnitudes for O- and B-type stars from the LAMOST spectra, which we combine with {it Gaia} parallaxes to infer distance and binarity. The method applies a neural network model trained on stars with precise {it Gaia} parallax to the spectra and predicts $K_{rm s}$-band absolute magnitudes $M_{Ks}$ with a precision of 0.25,mag, which corresponds to a precision of 12% in spectroscopic distance. For distant stars (e.g. $>5$,kpc), the inclusion of constraints from spectroscopic $M_{Ks}$ significantly improves the distance estimates compared to inferences from {it Gaia} parallax alone. Our method accommodates for emission line stars by first identifying them via PCA reconstructions and then treating them separately for the $M_{Ks}$ estimation. We also take into account unresolved binary/multiple stars, which we identify through deviations in the spectroscopic $M_{Ks}$ from the geometric $M_{Ks}$ inferred from {it Gaia} parallax. This method of binary identification is particularly efficient for unresolved binaries with near equal-mass components and thus provides an useful supplementary way to identify unresolved binary or multiple-star systems. We present a catalog of spectroscopic $M_{Ks}$, extinction, distance, flags for emission lines, and binary classification for 16,002 OB stars from LAMOST DR5. As an illustration of the method, we determine the $M_{Ks}$ and distance to the enigmatic LB-1 system, where Liu et al. (2019) had argued for the presence of a black hole and incorrect parallax measurement, and we do not find evidence for errorneous {it Gaia} parallax.
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Binary stars plays important role in the evolution of stellar populations . The intrinsic binary fraction ($f_{bin}$) of O and B-type (OB) stars in LAMOST DR5 was investigated in this work. We employed a cross-correlation approach to estimate relative radial velocities for each of the stellar spectra. The algorithm described by cite{2013A&A...550A.107S} was implemented and several simulations were made to assess the performance of the approach. Binary fraction of the OB stars are estimated through comparing the uni-distribution between observations and simulations with the Kolmogorov-Smirnov tests. Simulations show that it is reliable for stars most of whom have $6,7$ and $8$ repeated observations. The uncertainty of orbital parameters of binarity become larger when observational frequencies decrease. By adopting the fixed power exponents of $pi=-0.45$ and $kappa=-1$ for period and mass ratio distributions, respectively, we obtain that $f_{bin}=0.4_{-0.06}^{+0.05}$ for the samples with more than 3 observations. When we consider the full samples with at least 2 observations, the binary fraction turns out to be $0.37_{-0.03}^{+0.03}$. These two results are consistent with each other in $1sigma$.
Since September 2018, LAMOST starts a new 5-year medium-resolution spectroscopic survey (MRS) using bright/gray nights. We present the scientific goals of LAMOST-MRS and propose a near optimistic strategy of the survey. A complete footprint is also provided. Not only the regular medium-resolution survey, but also a time-domain spectroscopic survey is being conducted since 2018 and will be end in 2023. According to the detailed survey plan, we expect that LAMOST-MRS can observe about 2 million stellar spectra with ~7500 and limiting magnitude of around G=15 mag. Moreover, it will also provide about 200 thousand stars with averagely 60-epoch observations and limiting magnitude of G~14 mag. These high quality spectra will give around 20 elemental abundances, rotational velocities, emission line profiles as well as precise radial velocity with uncertainty less than 1 km/s. With these data, we expect that LAMOST can effectively leverage sciences on stellar physics, e.g. exotic binary stars, detailed observation of many types of variable stars etc., planet host stars, emission nebulae, open clusters, young pre-main-sequence stars etc.
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}.
We present a hierarchical probabilistic model for improving geometric stellar distance estimates using color--magnitude information. This is achieved with a data driven model of the color--magnitude diagram, not relying on stellar models but instead on the relative abundances of stars in color--magnitude cells, which are inferred from very noisy magnitudes and parallaxes. While the resulting noise-deconvolved color--magnitude diagram can be useful for a range of applications, we focus on deriving improved stellar distance estimates relying on both parallax and photometric information. We demonstrate the efficiency of this approach on the 1.4 million stars of the Gaia TGAS sample that also have APASS magnitudes. Our hierarchical model has 4~million parameters in total, most of which are marginalized out numerically or analytically. We find that distance estimates are significantly improved for the noisiest parallaxes and densest regions of the color--magnitude diagram. In particular, the average distance signal-to-noise ratio and uncertainty improve by 19~percent and 36~percent, respectively, with 8~percent of the objects improving in SNR by a factor greater than 2. This computationally efficient approach fully accounts for both parallax and photometric noise, and is a first step towards a full hierarchical probabilistic model of the Gaia data.
We present 22,901 OB spectra of 16,032 stars identified from LAMOST DR5 dataset. A larger sample of OB candidates are firstly selected from the distributions in the spectral line indices space. Then all 22,901 OB spectra are identified by manual inspection. Based on a sub-sample validation, we find that the completeness of the OB spectra reaches about $89pm22$% for the stars with spectral type earlier than B7, while around $57pm16$% B8--B9 stars are identified. The smaller completeness for late B stars is lead to the difficulty to discriminate them from A0--A1 type stars. The sub-classes of the OB samples are determined using the software package MKCLASS. With a careful validation using 646 sub-samples, we find that MKCLASS can give fairly reliable sub-types and luminosity class for most of the OB stars. The uncertainty of the spectral sub-type is around 1 sub-type and the uncertainty of the luminosity class is around 1 level. However, about 40% of the OB stars are failed to be assigned to any class by MKCLASS and a few spectra are significantly misclassified by MKCLASS. This is likely because that the template spectra of MKCLASS are selected from nearby stars in the solar neighborhood, while the OB stars in this work are mostly located in the outer disk and may have lower metallicity. The rotation of the OB stars may also be responsible for the mis-classifications. Moreover, we find that the spectral and luminosity classes of the OB stars located in the Galactic latitude larger than 20$^circ$ are substantially different with those located in latitude smaller than 20$^circ$, which may either due to the observational selection effect or hint a different origin of the high Galactic latitude OB stars.
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