LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS): Scientific goals and survey plan

Liu, Chao; Fu, Jianning; Shi, Jianrong; Wu, Hong; Han, Zhanwen; Chen, Li; Dong, Subo; Zhao, Yi; Chen, Jianjun; Zhang, Haotong; Bai, Zhong-Rui; Chen, Xuefei; Cui, Wenyuan; Du, Bing; Hsia, Chih-Hao; Jiang, Dengkai; Hou, Jinliang; Wen, Hongbo; Li, Haining; Li, Jiao; Li, Lifang; Liu, Jiaming; Liu, Jifeng; Luo, A-Li; Ren, Juanjuan; Wang, Jiaxin; Wu, Chao-Jian; Xie, Ji-Wei; Yan, Hong-Liang; Yang, Fan; Yu, Jincheng; Zhang, Bo; Zhang, Huawei; Zhang, Liyun; Zhang, Wei; Zhao, Gang; Zhong, Jing; Zong, Weikai; Zuo, Fang

doi:10.48550/arxiv.2005.07210

Cited by 84 publications

(119 citation statements)

References 57 publications

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“…The MRS has R ∼ 7500 and includes both a blue arm (B) and a red arm (R). Spectral observations made from the blue arm spanning wavelength in a range of 4950 ∼ 5350 Å and the spectra observed using the red arm cover a wavelength range of 6300 ∼ 6800 Å (Liu et al 2020). LAM-OST finished the first observing campaign from 2011 to 2018 (known as LAMOST I).…”

Section: Lamost Data Selectionmentioning

confidence: 99%

“…The second active survey program (LAMOST II) began in 2017 September. A total number of about five million medium-resolution spectra observed with a single exposure for ∼800,000 stars were collected by 2019 June (Liu et al 2020). Liu et al (2019) identified 16,032 early-type stars through measuring the equivalent widths of several absorption line profiles using low-resolution spectra from the LAMOST LRS database.…”

Section: Lamost Data Selectionmentioning

confidence: 99%

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The Early-type Stars from LAMOST survey: Atmospheric parameters

Guo,

Zhang,

Liu

et al. 2021

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Massive stars play key roles in many astrophysical processes. Deriving atmospheric parameters of massive stars is important to understand their physical properties and thus are key inputs to trace their evolution. Here we report our work on adopting the data-driven technique Stellar LAbel Machine (SLAM) with the non-LTE TLUSTY synthetic spectra as the training dataset to estimate the stellar parameters of LAMOST optical spectra for early-type stars. We apply two consistency tests to verify this machine learning method and compare stellar labels given by SLAM with that in literature for several objects having high-resolution spectra. We provide the stellar labels of effective temperature (T eff ), surface gravity (log g), metallicity ([M/H]), and projected rotational velocity (v sin i) for 3,931 and 578 early-type stars from LAMOST Low-Resolution Survey (LAMOST-LRS) and Medium-Resolution Survey (LAMOST-MRS), respectively. To estimate the average statistical uncertainties of our results, we calculated the standard deviation between the predicted stellar label and the pre-labeled published values from the high-resolution spectra. The uncertainties of the four parameters are σ(T eff ) = 2, 185K, σ(log g) = 0.29 dex, and σ(v sin i) = 11 km s −1 for MRS, and σ(T eff ) = 1, 642K, σ(log g) = 0.25 dex, and σ(v sin i) = 42 km s −1 for LRS spectra, respectively. We notice that parameters of T eff , log g and [M/H] can be better constrained using LRS spectra rather than using MRS spectra, most likely due to their broad wavelength coverage, while v sin i is constrained better by MRS spectra than by LRS spectra, probably due to the relatively accurate line profiles of MRS spectra.

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Section: Lamost Data Selectionmentioning

confidence: 99%

Section: Lamost Data Selectionmentioning

confidence: 99%

The Early-type Stars from LAMOST survey: Atmospheric parameters

Guo,

Zhang,

Liu

et al. 2021

Preprint

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“…The LAMOST MRS-N, (Wu et al, 2020;Wu et al, 2021), as a sub-project of the Medium-Resolution Spectral Survey (MRS, (Liu et al, 2020)), mainly relies on the LAMOST (also known as Guoshoujing Telescope), which is the first astronomical large-scale scientific facility of China (Cui et al, 2012;Zhao et al, 2012;Wang et al, 1996;Su & Cui, 2004) and has achieved great success in many fields of astronomy (Liu et al, 2020;Gao et al, 2014;Liu et al, 2015;Karoff et al, 2016;Luo et al, 2016;Wang et al, 2017b;Ren et al, 2018b;Liu, 2019;Liu et al, 2019;Gu et al, 2019;Wu et al, 2016Wu et al, , 2020Tian et al, 2015;Xiang et al, 2015a;Huang et al, 2016;Liu et al, 2017a,b;Tian et al, 2017;Wang et al, 2017a;Li et al, 2018;Tian et al, 2018;Wang et al, 2018;Xu et al, 2018;Yu & Liu, 2018;Zhao et al, 2018;Li et al, 2019;Tian et al, 2019;Wang et al, 2019b,a;Shen et al, 2016;Wu et al, 2010a,b;Huang et al, 2019), to observe the nebulae (including the H II regions, Herbig-Haro objects, supernova remnants, planetary nebulae) in the northern Galactic Plane (GP). From October 2018 to now, MRS-N has been conducted for three years and obtained more than 190 thousand medium resolution spectra of Galactic nebulae.…”

Section: Introductionmentioning

confidence: 99%

The Data Processing of the LAMOST Medium-Resolution Spectral Survey of Galactic Nebulae (LAMOST MRS-N Pipeline)

Wu,

Zhang

et al. 2021

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The Large sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) medium-resolution spectral survey of Galactic Nebulae (MRS-N) has conducted for three years since Sep. 2018 and observed more than 190 thousands nebular spectra and 20 thousands stellar spectra. However, there is not yet a data processing pipeline for nebular data. To significantly improve the accuracy of nebulae classification and their physical parameters, we developed the MRS-N Pipeline. This article presented in detail each data processing step of the MRS-N Pipeline, such as removing cosmic rays, merging single exposure, fitting sky light emission lines, subtracting skylight, wavelength recalibration, measuring nebular parameters, creating catalogs and packing spectra. Finally, a description of the data products, including nebular spectra files and parameter catalogs, is provided.

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“…The LAMOST medium-resolution survey (MRS, R ∼ 7 500, Liu et al 2020), started from Sep. 2017, is one of the pioneer time-domain spectroscopic surveys. The blue and red arms cover Mg b triplet (4950−5350 Å) and Hα (6300−6800 Å), respectively.…”

Section: Introductionmentioning

confidence: 99%

The Spectroscopic Binaries from LAMOST Medium-Resolution Survey (MRS). I. Searching for Double-lined Spectroscopic Binaries (SB2s) with Convolutional Neural Network

Zhang,

Jing,

Yang

et al. 2021

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We developed a convolutional neural network (CNN) model to distinguish the double-lined spectroscopic binaries (SB2s) from others based on single exposure medium-resolution spectra (R ∼ 7, 500).The training set consists of a large set of mock spectra of single stars and binaries synthesized based on the MIST stellar evolutionary model and ATLAS9 atmospheric model. Our model reaches a novel theoretic false positive rate by adding a proper penalty on the negative sample (e.g., 0.12% and 0.16% for the blue/red arm when the penalty parameter Λ = 16). Tests show that the performance is as expected and favors FGK-type Main-sequence binaries with high mass ratio (q ≥ 0.7) and large radial velocity separation (∆v ≥ 50 km s −1 ). Although the real false positive rate can not be estimated reliably, validating on eclipsing binaries identified from Kepler light curves indicates that our model predicts low binary probabilities at eclipsing phases (0, 0.5, and 1.0) as expected. The color-magnitude diagram also helps illustrate its feasibility and capability of identifying FGK MS binaries from spectra. We conclude that this model is reasonably reliable and can provide an automatic approach to identify SB2s with period 10 days. This work yields a catalog of binary probabilities for over 5 million spectra of 1 million sources from the LAMOST medium-resolution survey (MRS), and a catalog of 2198 SB2 candidates whose physical properties will be analyzed in our following-up paper. Data products are made publicly available at the journal as well as our Github website.

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LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS): Scientific goals and survey plan

Abstract: 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 pro-

Cited by 84 publications

References 57 publications

The Early-type Stars from LAMOST survey: Atmospheric parameters

The Early-type Stars from LAMOST survey: Atmospheric parameters

The Data Processing of the LAMOST Medium-Resolution Spectral Survey of Galactic Nebulae (LAMOST MRS-N Pipeline)

The Spectroscopic Binaries from LAMOST Medium-Resolution Survey (MRS). I. Searching for Double-lined Spectroscopic Binaries (SB2s) with Convolutional Neural Network

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