arXiv subjects

Bing Du

Publications and source records attributed to Bing Du.

At least 19 recordsLinked to original sources

arXiv2026

SFC-Aware Online Aggregated Data-Link Orchestration for SDN/NFV-Enabled SAGINs

Civil aviation space-air-ground integrated networks (SAGINs) are expected to support heterogeneous cockpit and cabin services over dynamic air-to-air (A2A), air-to-ground (A2G), and air-to-satellite (A2S) data links. This paper studies service function chain (SFC)-aware online access-side aggregated data-link orchestration for civil aviation SAGINs enabled by software-defined networking and network function virtualization (SDN/NFV). We jointly orchestrate spatial bearer resources and temporal elasticity enabled by temporal elastic mapping and parking (TEMP). A rolling-slot model is developed with four request-level actions: NOW, TEMP, REJECT, and DROP, under a lexicographic objective that prioritizes service success, then normalized access-orchestration delay, and finally residual TEMP-related risk. To avoid exhaustive search over the full binary action space, we propose Model-Induced Risk and Scarcity-Aware Refinement (MRSAR), which derives deferred-realization risk from the success-prioritized objective and resource-scarcity signals from data-link and TEMP buffer constraints. These signals guide valuation-based feasible construction and bounded neighborhood refinement. Simulation results show that MRSAR remains close to the Gurobi-MILP reference in service success, outperforms arrival-order and delay-myopic greedy baselines, controls REJECT and DROP failures, and achieves a favorable quality-complexity tradeoff for rolling online orchestration.

cs.NI↗

arXiv2026

Locate a stellar flare from the M dwarf LAMOST J1332+5057

Young M-type stars exhibit frequent flares, which would seriously impact their habitable planets. Since stellar surfaces cannot be resolved, flare locations remain unknown. Here, by using the Mg I b emission line in LAMOST spectra, the location of a stellar flare from a young M dwarf is pinpointed at $ (-123.0^{+8.0}_{-5.8}, 80.5^{+2.9}_{-3.2})$ in degree in the polar region. Our method can be used to accurately locate stellar flares. This would enable us to assess the impact of stellar flares on planets more accurately and improve our understanding of stellar dynamo models.

astro-ph.SR↗

arXiv2025

Scalable Stellar Parameter Inference Using Python-based LASP: From CPU Optimization to GPU Acceleration

To enhance the efficiency, scalability, and cross-survey applicability of stellar parameter inference in large spectroscopic datasets, we present a modular, parallelized Python framework with automated error estimation, built on the LAMOST Atmospheric Parameter Pipeline (LASP) originally implemented in IDL. Rather than a direct code translation, this framework refactors LASP with two complementary modules: LASP-CurveFit, a new implementation of the LASP fitting procedure that runs on a CPU, preserving legacy logic while improving data I/O and multithreaded execution efficiency; and LASP-Adam-GPU, a GPU-accelerated method that introduces grouped optimization by constructing a joint residual function over multiple observed and model spectra, enabling high-throughput parameter inference across tens of millions of spectra. Applied to 10 million LAMOST spectra, the framework reduces runtime from 84 to 48 hr on the same CPU platform and to 7 hr on an NVIDIA A100 GPU, while producing results consistent with those from the original pipeline. The inferred errors agree well with the parameter variations from repeat observations of the same target (excluding radial velocities), while the official empirical errors used in LASP are more conservative. When applied to DESI DR1, our effective temperatures and surface gravities agree better with APOGEE than those from the DESI pipeline, particularly for cool giants, while the latter performs slightly better in radial velocity and metallicity. These results suggest that the framework delivers reliable accuracy, efficiency, and transferability, offering a practical approach to parameter inference in large spectroscopic surveys. The code and DESI-based catalog are available via \dataset[DOI: 10.12149/101679]{https://doi.org/10.12149/101679} and \dataset[DOI: 10.12149/101675]{https://doi.org/10.12149/101675}, respectively.

astro-ph.GA↗

arXiv2024

Estimating Stellar Atmospheric Parameters and [{\alpha}/Fe] for LAMOST O-M type Stars Using a Spectral Emulator

In this paper, we developed a spectral emulator based on the Mapping Nearby Galaxies at Apache Point Observatory Stellar Library (MaStar) and a grouping optimization strategy to estimate effective temperature (T_eff), surface gravity (log g), metallicity ([Fe/H]) and the abundance of alpha elements with respect to iron ([alpha/Fe]) for O-M-type stars within the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) low-resolution spectra. The primary aim is to use a rapid spectral-fitting method, specifically the spectral emulator with the grouping optimization strategy, to create a comprehensive catalog for stars of all types within LAMOST, addressing the shortcomings in parameter estimations for both cold and hot stars present in the official LAMOST AFGKM-type catalog. This effort is part of our series of studies dedicated to establishing an empirical spectral library for LAMOST. Experimental results demonstrate that our method is effectively applicable to parameter prediction for LAMOST, with the single-machine processing time within $70$ hr. We observed that the internal error dispersions for T_eff, log g, [Fe/H], and [alpha/Fe] across different spectral types lie within the ranges of $15-594$ K, $0.03-0.27$ dex, $0.02-0.10$ dex, and $0.01-0.04$ dex, respectively, indicating a good consistency. A comparative analysis with external data highlighted deficiencies in the official LAMOST catalog and issues with MaStar parameters, as well as potential limitations of our method in processing spectra with strong emission lines and bad pixels. The derived atmospheric parameters as a part of this work are available at https://nadc.china-vo.org/res/r101402/ .

astro-ph.SR↗

arXiv2024

An Empirical Sample of Spectra of M-type Stars with Homogeneous Atmospheric-Parameter Labels

The discrepancies between theoretical and observed spectra, and the systematic differences between various spectroscopic parameter estimates, complicate the determination of atmospheric parameters of M-type stars. In this work, we present an empirical sample of 5105 M-type star spectra with homogeneous atmospheric parameter labels through stellar-label transfer and sample cleaning. We addressed systematic discrepancies in spectroscopic parameter estimates by adopting recent results for Gaia EDR3 stars as a reference standard. Then, we used a density-based spatial clustering of applications with noise to remove unreliable samples in each subgrid of parameters. To confirm the reliability of the stellar labels, a 5-layer neural network was utilized, randomly partitioning the samples into training and testing sets. The standard deviations between the predicted and actual values in the testing set are 14 K for Teff , 0.06 dex for log g, and 0.05 dex for [M/H], respectively. In addition, we conducted an internal cross-validation to enhance validation and obtained precisions of 11 K, 0.05 dex, and 0.05 dex for Teff , log g, and [M/H], respectively. A grid of 1365 high Signal-to-Noise ratio (S/N) spectra and their labels, selected from the empirical sample, was utilized in the stellar parameter pipeline for M-Type stars (LASPM) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST), producing an almost seamless Kiel distribution diagram for LAMOST DR10 and DR11 data. The atmospheric parameters for M-type stars from LAMOST DR11 show improved precision compared to the data from DR9, with improvements (for spectra with S/N higher than 10) from 118 to 67 K in Teff , 0.2 to 0.07 dex in log g, and 0.29 to 0.14 dex in [M/H].

astro-ph.SR↗

arXiv2024

Projected rotational velocities for LAMOST stars with effective temperature lower than 9000 K

In Data Release 9 of LAMOST, we present measurements of v sin i for a total of 121,698 stars measured using the Medium Resolution Spectrograph (MRS) and 80,108 stars using the Low Resolution Spectrograph (LRS). These values were obtained through a chi^2 minimisation process, comparing LAMOST spectra with corresponding grids of synthetically broadened spectra. Due to the resolution and the spectral range of LAMOST, v sin i measurements are limited to stars with effective temperature (Teff) ranging from 5000 K to 8500 K for MRS and 7000 K to 9000 K for LRS. The detectable v sin i for MRS is set between 27 km/s and 350 km/s , and for LRS between 110 km/s and 350 km/s, This limitation is because the convolved reference spectra become less informative beyond 350 km/s. The intrinsic precisions of v sin i , determined from multi-epoch observations, is approximately 4.0 km/s for MRS and 10.0 km/s for LRS at signal-to-noise ratio (S/N) greater than 50. Our v sin i values show consistence with those from APOGEE17, displaying a scatter of 8.79 km/s. They are also in agreement with measurements from the Gaia DR3 and SUN catalogs. An observed trend in LAMOST MRS data is the decrease in v sin i with dropping Teff, particularly transiting around 7000 K for dwarfs and 6500 K for giants, primarily observed in stars with near-solar abundances.

astro-ph.SR↗

arXiv2023

Stellar Atmospheric Parameters for Cool Dwarfs in Gaia DR3

We provide a catalogue of atmospheric parameters for 1,806,921 cool dwarfs from Gaia DR3 which lie within the range covered by LAMOST cool dwarf spectroscopic parameters: 3200 K < T_{eff}< 4300 K, -0.8 < [M/H] < 0.2 dex, and 4.5 <log{g} < 5.5 dex. Our values are derived based on Machine Learning models trained with multi-band photometry corrected for dust. The photometric data comprises of optical from SDSS r, i, z bands, near-infrared from 2MASS J, H, K and mid-infrared from ALLWISE W1, W2. We used both random forest and LightGBM machine learning models and found similar results from both with an error dispersion of 68 K, 0.22 dex, and 0.05 dex for T_{eff}, [M/H], and log {g}, respectively. Assessment of the relative feature importance of different photometric colors indicated W1 -- W2 as most sensitive to both T_{eff} and log{g}, with J -- H most sensitive to [M/H]. We find that our values show a good agreement with APOGEE, but are significantly different to those provided as part of Gaia DR3.

astro-ph.SR↗

arXiv2022

Searching for Barium Stars from the LAMOST Spectra Using the Machine Learning Method: I

Barium stars are chemically peculiar stars that exhibit enhancement of s-process elements. Chemical abundance analysis of barium stars can provide crucial clues for the study of the chemical evolution of the Galaxy. The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has released more than 6 million low-resolution spectra of FGK-type stars by Data Release 9 (DR9), which can significantly increase the sample size of barium stars. In this paper, we used machine learning algorithms to search for barium stars from low-resolution spectra of LAMOST. We have applied the Light Gradient Boosting Machine (LGBM) algorithm to build classifiers of barium stars based on different features, and build predictors for determining [Ba/Fe] and [Sr/Fe] of barium candidates. The classification with features in the whole spectrum performs best: for the sample with strontium enhancement, Precision = 97.81%, and Recall = 96.05%; for the sample with barium enhancement, Precision = 96.03% and Recall = 97.70%. In prediction, [Ba/Fe] estimated from BaII line at 4554 Å has smaller dispersion than that from BaII line at 4934 Å: MAE$_{4554 Å}$ = 0.07, $σ_{4554 Å}$ = 0.12. [Sr/Fe] estimated from SrII line at 4077 Å performs better than that from SrII line at 4215 Å: MAE$_{4077 Å}$ = 0.09, $σ_{4077 Å}$ = 0.16. A comparison of the LGBM and other popular algorithms shows that LGBM is accurate and efficient in classifying barium stars. This work demonstrated that machine learning can be used as an effective means to identify chemically peculiar stars and determine their elemental abundance.

astro-ph.SR↗

arXiv2022

Identification and parameter determination of F-type Herbig stars from LAMOST DR8

We identify 20 F-type Herbig stars and provide a list of 22 pre-main-sequence candidates from LAMOST DR8. The effective temperature, distance, extinction, stellar luminosity, mass, and radius are derived for each Herbig star based on optical spectra, photometry, Gaia EDR3 parallaxes, and pre-main-sequence evolutionary tracks. According to spectral energy distributions, 19 F-type Herbig stars belong to Class II YSOs, and one belongs to the flat-spectrum class. Four have Spitzer IRS spectra, of which three show extremely weak polycyclic aromatic hydrocarbons emissions, and three with both amorphous and crystalline silicate emissions share the similar parameters and are at the same evolutionary stage. We detect a solar-nearby outbursting EXor Herbig star J034344.48+314309.3, possible precursor of a Herbig Ae star. Intense emission lines of HI, HeI, OI, NaI, and CaII originated from the rapid accretion during the outbursts are detected in its optical spectra, and silicate emission features are detected in its infrared spectrum. We also make a statistic analysis on the disk properties of all known Herbig stars using the defined infrared spectral indices. The proportion of Herbig stars with moderate infrared excesses decreases as effective temperature increases. The majority of the precursors (F-, G-, or K- type) have moderate infrared excesses. Hotter Herbig stars tend to have a larger proportion with large infrared excesses. The trends may be due to the fact that hotter stars have larger areas of re-emitting dust, although there is some scatter due to the particularities of each disk.

astro-ph.SR↗

arXiv2022

Estimating Atmospheric Parameters from LAMOST Low-Resolution Spectra with Low SNR

Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) acquired tens of millions of low-resolution stellar spectra. The large amount of the spectra result in the urgency to explore automatic atmospheric parameter estimation methods. There are lots of LAMOST spectra with low signal-to-noise ratios (SNR), which result in a sharp degradation on the accuracy of their estimations. Therefore, it is necessary to explore better estimation methods for low-SNR spectra. This paper proposed a neural network-based scheme to deliver atmospheric parameters, LASSO-MLPNet. Firstly, we adopt a polynomial fitting method to obtain pseudo-continuum and remove it. Then, some parameter-sensitive features in the existence of high noises were detected using Least Absolute Shrinkage and Selection Operator (LASSO). Finally, LASSO-MLPNet used a Multilayer Perceptron network (MLPNet) to estimate atmospheric parameters $T_{\mathrm{eff}}$, log $g$ and [Fe/H]. The effectiveness of the LASSO-MLPNet was evaluated on some LAMOST stellar spectra of the common star between APOGEE (The Apache Point Observatory Galactic Evolution Experiment) and LAMOST. it is shown that the estimation accuracy is significantly improved on the stellar spectra with $10<\mathrm{SNR}\leq80$. Especially, LASSO-MLPNet reduces the mean absolute error (MAE) of the estimation of $T_{\mathrm{eff}}$, log $g$ and [Fe/H] from (144.59 K, 0.236 dex, 0.108 dex) (LASP) to (90.29 K, 0.152 dex, 0.064 dex) (LASSO-MLPNet) on the stellar spectra with $10<\mathrm{SNR}\leq20$. To facilitate reference, we release the estimates of the LASSO-MLPNet from more than 4.82 million stellar spectra with $10<\mathrm{SNR}\leq80$ and 3500 < SNR$g$ $\leq$ 6500 as a value-added output.

astro-ph.IM↗

arXiv2022

Estimation of stellar atmospheric parameters from LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30

The accuracy of the estimated stellar atmospheric parameter decreases evidently with the decreasing of spectral signal-to-noise ratio (SNR) and there are a huge amount of this kind observations, especially in case of SNR$<$30. Therefore, it is helpful to improve the parameter estimation performance for these spectra and this work studied the ($T_\texttt{eff}, \log~g$, [Fe/H]) estimation problem for LAMOST DR8 low-resolution spectra with 20$\leq$SNR$<$30. We proposed a data-driven method based on machine learning techniques. Firstly, this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator (LASSO), rejected ineffective data components and irrelevant data. Secondly, a Multi-layer Perceptron (MLP) method was used to estimate stellar atmospheric parameters from the LASSO features. Finally, the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the APOGEE (Apache Point Observatory Galactic Evolution Experiment) high-resolution spectra. Experiments show that the Mean Absolute Errors (MAE) of $T_\texttt{eff}, \log~g$, [Fe/H] are reduced from the LASP (137.6 K, 0.195 dex, 0.091 dex) to LASSO-MLP (84.32 K, 0.137 dex, 0.063 dex), which indicate evident improvements on stellar atmospheric parameter estimation. In addition, this work estimated the stellar atmospheric parameters for 1,162,760 low-resolution spectra with 20$\leq$SNR$<$30 from LAMOST DR8 using LASSO-MLP, and released the estimation catalog, learned model, experimental code, trained model, training data and test data for scientific exploration and algorithm study.

astro-ph.GA↗

arXiv2022

S-type stars discovered in Medium-Resolution Spectra of LAMOST DR9

In this paper, we report on 606 S-type stars identified from Data Release 9 of the LAMOST medium-resolution spectroscopic (MRS) survey, and 539 of them are reported for the first time. The discovery of these stars is a three-step process, i.e., selecting with the ZrO band indices greater than 0.25, excluding non-S-type stars with the iterative Support Vector Machine method, and finally retaining stars with absolute bolometric magnitude larger than -7.1. The 606 stars are consistent with the distribution of known S-type stars in the color-magnitude diagram. We estimated the C/Os using the [C/Fe] and [O/Fe] provided by APOGEE and the MARCS model for S-type stars, respectively, and the results of the two methods show that C/Os of all stars are larger than 0.5. Both the locations on the color-magnitude diagram and C/Os further verify the nature of our S-type sample. Investigating the effect of TiO and atmospheric parameters on ZrO with the sample, we found that log g has a more significant impact on ZrO than Teff and [Fe/H], and both TiO and log g may negatively correlate with ZrO. According to the criterion of Tian et al. (2020), a total of 238 binary candidates were found by the zero-point-calibrated radial velocities from the officially released catalog of LAMOST MRS and the catalog of Zhang et al. (2021). A catalog of these 606 S-type stars is available from the following link https://doi.org/10.12149/101097.

astro-ph.SR↗

arXiv2022

Ultracool dwarfs identified using spectra in LAMOST DR7

In this work, we identify 734 ultracool dwarfs with a spectral type of M6 or later, including one L0. Of this sample, 625 were studied spectroscopically for the first time. All of these ultracool dwarfs are within 360~pc, with a \textit{Gaia} G magnitude brighter than ~19.2 mag. By studying the spectra and checking their stellar parameters (Teff, logg, and [FeH] derived with the LAMOST pipeline, we found their cool red nature and their metallicity to be consistent with the nature of Galactic thin-disk objects. Furthermore, 77 of them show lithium absorption lines at 6708A, further indicating their young ages and substellar nature. Kinematics obtained through LAMOST radial velocities, along with the proper motion and parallax data from Gaia EDR3, also suggest that the majority of our targets are thin-disk objects. Kinematic ages were estimated through the relationship between the velocity dispersion and the average age for a certain population. Moreover, we identified 35 binaries, with 6 of them reported as binaries for the first time.

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arXiv2021

LAMOST Time-Domain Survey: First Results of four $K$2 plates

From Oct. 2019 to Apr. 2020, LAMOST performs a time-domain spectroscopic survey of four $K$2 plates with both low- and med-resolution observations. The low-resolution spectroscopic survey gains 282 exposures ($\approx$46.6 hours) over 25 nights, yielding a total of about 767,000 spectra, and the med-resolution survey takes 177 exposures ($\approx$49.1 hours) over 27 nights, collecting about 478,000 spectra. More than 70%/50% of low-resolution/med-resolution spectra have signal-to-noise ratio higher than 10. We determine stellar parameters (e.g., $T_{\rm eff}$, log$g$, [Fe/H]) and radial velocity (RV) with different methods, including LASP, DD-Payne, and SLAM. In general, these parameter estimations from different methods show good agreement, and the stellar parameter values are consistent with those of APOGEE. We use the $Gaia$ DR2 RV data to calculate a median RV zero point (RVZP) for each spectrograph exposure by exposure, and the RVZP-corrected RVs agree well with the APOGEE data. The stellar evolutionary and spectroscopic masses are estimated based on the stellar parameters, multi-band magnitudes, distances and extinction values. Finally, we construct a binary catalog including about 2700 candidates by analyzing their light curves, fitting the RV data, calculating the binarity parameters from med-resolution spectra, and cross-matching the spatially resolved binary catalog from $Gaia$ EDR3. The LAMOST TD survey is expected to get breakthrough in various scientific topics, such as binary system, stellar activity, and stellar pulsation, etc.

astro-ph.SR↗

arXiv2021

Study on Outliers in the Big Stellar Spectral Dataset of the Fifth Data Release (DR5) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)

To study the quality of stellar spectra of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) and the correctness of the corresponding stellar parameters derived by the LASP (LAMOST Stellar Parameter Pipeline), the outlier analysis method is applied to the archived AFGK stars in the fifth data release (DR5) of LAMOST. The outlier factor is defined in order to sort more than 3 million stellar spectra selected from the DR5 Stellar Parameter catalog. We propose an improved Local Outlier Factor (LOF) method based on Principal Component Analysis and Monte Carlo to enable the computation of the LOF rankings for randomly picked sub-samples that are computed in parallel by multiple computers, and finally to obtain the outlier ranking of each spectrum in the entire dataset. Totally 3,627 most outlier ranked spectra, around one-thousandth of all spectra, are selected and clustered into 10 groups, and the parameter density distribution of them conforms to the parameter distribution of LAMOST DR5, which suggests that in the whole parameter space the probability of bad spectra is uniformly distributed. By cross-matching the 3,627 spectra with APOGEE, we obtain 122 common ones. The published parameters calculated from LASP agree with APOGEE for the 122 spectra although there are bad pixels or bad flux calibrations in them. On the other hand, some outlier spectra show strong nebular contamination warning the corresponding parameters should be carefully used. A catalog and a spectral atlas of all the 3,627 outliers can be found at the link http://paperdata.china-vo.org/LY_paper/dr5Outlier/dr5Outlier_resource.zip.

astro-ph.SR↗

arXiv2020

M Subdwarf Research. II. Atmospheric Parameters and Kinematics

Applying the revised M subdwarf classification criteria discussed in Paper I to LAMOST DR7, combining the M subdwarf sample from Savcheva et al, a new M subdwarf sample was constructed for further study. The atmospheric parameters for each object were derived fitting with the PHOENIX grid, combining with Gaia DR2, the relationship between the gravity and metallicity were explored according to the locus both in the color-absolute magnitude diagram and the reduced proper motion diagram. Objects that have both the largest gravity and the lowest metallicity are located away from the main-sequence cloud and may be considered as the intrinsic M subdwarfs, which can be classified as luminosity class VI. Another group of objects whose spectra show typical M subdwarf characters have lower gravity and relatively moderate metal deficiency and occupy part of the ordinary M dwarf region in both diagrams. The Galactic U , V , W space velocity components and their dispersion show that the local Galactic halo population sampled in the solar neighborhood is represented by objects of high gravity and an inconspicuous bimodal metallicity distribution, with a fraction of prograde orbits. The other M subdwarfs seem to partly belong to the thick disk component with a significant fraction of thin disk moderately metal-poor objects intricately mixed with them. However, the selection effects, especially the favored anti-center direction of investigation in the LAMOST sub-sample, but also contamination by multiplicity and parameter coupling could play important roles and need to be further investigated.

astro-ph.SR↗

arXiv2020

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

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.

astro-ph.SR↗

arXiv2019

Radial velocity measurements from LAMOST medium-resolution spectroscopic observations: A pointing towards the Kepler field

Radial velocity is one of key measurements in understanding the fundamental properties of stars, stellar clusters and the Galaxy. A plate of stars in the Kepler field were observed in May of 2018 with the medium-resolution spectrographs of LAMOST, aiming to test the performance of this new system which is the upgraded equipment of LAMOST after the first five-year regular survey.We present our analysis on the radial velocity measurements (RVs) derived from these data. The results show that slight and significant systematic errors exist among the RVs obtained from the spectra collected by different spectrographs and exposures, respectively. After correcting the systematic errors with different techniques, the precision of RVs reaches ~1.3, ~1.0, ~0.5 and ~0.3 km/s at S/Nr = 10, 20, 50, and 100, respectively. Comparing with the RVs of the standard stars of the APOGEE survey, our RVs are calibrated with a zero-point shift of ~7 km/s. The results indicate that the LAMOST medium-resolution spectroscopic system may provide RVs in a reasonable accuracy and precision for the selected targets.

astro-ph.SR↗