Mapping the Milky Way with LAMOST I: method and overview

Cui

et al. 2019

ApJS

Self Cite

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 89 ± 22% for the stars with spectral type earlier than B7, while around 57 ± 16% 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 • are substantially different with those located in latitude smaller than 20 • , which may either due to the observational selection effect or hint a different origin of the high Galactic latitude OB stars.

Section: Line Indicesmentioning

confidence: 99%

A Catalog of OB Stars from LAMOST Spectroscopic Survey

Cui

et al. 2019

ApJS

Self Cite

“…where ω θ , ω ∆d , w ∆V los , w ∆V l , w ∆V b , and the indices (i, j) are exactly the same as in δ 6d , but (i r , j r ) are shuffling indices. The selection function of LAMOST K giants varies with line-of-sight (Liu et al 2017). However, it is a reasonable assumption that the distance of the stars in the same part of the sky are uncorrelated to the sample selection.…”

Section: The Diffuse Halo Systemmentioning

confidence: 99%

Identifying Galactic Halo Substructure in 6D Phase Space Using ∼13,000 LAMOST K Giants

Yang

Xue²,

et al. 2019

ApJ

Self Cite

We construct a large halo K-giant sample by combining the positions, distances, radial velocities, and metallicities of over 13, 000 LAMOST DR5 halo K giants with the Gaia DR2 proper motions, which covers a Galactocentric distance range of 5−120 kpc. Using a position-velocity clustering estimator (the 6Distance), we statistically quantify the presence of position-velocity substructure at high significance: K giants have more close pairs in position-velocity space than a smooth stellar halo. We find that the amount of substructure in the halo increases with increasing distance and metallicity. With a percolation algorithm named friends-of-friends (FoF) to identify groups, we identify members belonging to Sagittarius (Sgr) Streams, Monoceros Ring, Virgo overdensity,

“…This results from the limited dynamic range of magnitudes observed on a single LAMOST plate and from the brighter r magnitude limit at the faint end compared to the designed goal (Liu et al 2017). Finally, the target selection was carried out on a plate-by-plate basis with different plates covering 9 < r < 14, 14 < r < 16.8, r < 17.8, and r < 18.5.…”

Section: Lamostmentioning

confidence: 99%

Effects of the selection function on metallicity trends in spectroscopic surveys of the Milky Way

Nandakumar

Schultheis

Hayden

et al. 2017

A&A

Context. Large spectroscopic Galactic surveys imply a selection function in the way they performed their target selection. Aims. We investigate here the effect of the selection function on the metallicity distribution function (MDF) and on the vertical metallicity gradient by studying similar lines of sight using four different spectroscopic surveys (APOGEE, LAMOST, RAVE, and Gaia-ESO), which have different targeting strategies and therefore different selection functions. Methods. We use common fields between the spectroscopic surveys of APOGEE, LAMOST, RAVE (ALR) and APOGEE, RAVE, Gaia-ESO (AGR) and use two stellar population synthesis models, GALAXIA and TRILEGAL, to create mock fields for each survey. We apply the selection function in the form of colour and magnitude cuts of the respective survey to the mock fields to replicate the observed source sample. We make a basic comparison between the models to check which best reproduces the observed sample distribution. We carry out a quantitative comparison between the synthetic MDF from the mock catalogues using both models to understand the effect of the selection function on the MDF and on the vertical metallicity gradient. Results. Using both models, we find a negligible effect of the selection function on the MDF for APOGEE, LAMOST, and RAVE. We find a negligible selection function effect on the vertical metallicity gradients as well, though GALAXIA and TRILEGAL have steeper and shallower slopes, respectively, than the observed gradient. After applying correction terms on the metallicities of RAVE and LAMOST with respect to our reference APOGEE sample, our observed vertical metallicity gradients between the four surveys are consistent within 1σ. We also find consistent gradient for the combined sample of all surveys in ALR and AGR. We estimated a mean vertical metallicity gradient of −0.241 ± 0.028 dex kpc −1 . There is a significant scatter in the estimated gradients in the literature, but our estimates are within their ranges. Conclusions. We have shown that there is a negligible selection function effect on the MDF and the vertical metallicity gradients for APOGEE, RAVE, and LAMOST using two stellar population synthesis models. Therefore, it is indeed possible to combine common fields of different surveys in studies using MDF and metallicity gradients provided their metallicities are brought to the same scale.