Automatic determination of stellar atmospheric parameters and construction of stellar spectral templates of the Guoshoujing Telescope (LAMOST)

Wu, Yue; Luo, A-Li; Li, Haining; Shi, Jianrong; Prugniel, Philippe; Liang, Y. C.; Zhao, Yi; Zhang, Jiannan; Bai, Zhong-Rui; Wei, Peng; Dong, Weixiang; Zhang, Haotong; Chen, Jianjun

doi:10.1088/1674-4527/11/8/006

Cited by 225 publications

(191 citation statements)

References 87 publications

Supporting

Mentioning

189

Contrasting

Order By: Relevance

“…However, only about half of the total data are high quality F, G, and K type spectra, for which stellar parameters are estimated using Ulyss (Wu et al 2011) in the pipeline. Ulyss is a forward model method, which models the spectrum pixels as a linear combination of a set of non-linear components.…”

Section: Lamost Survey Datamentioning

confidence: 99%

The K Giant Stars From the Lamost Survey Data. I. Identification, Metallicity, and Distance

Liu

Deng

Carlin

et al. 2014

ApJ

Self Cite

View full text Add to dashboard Cite

We present a support vector machine classifier to identify the K giant stars from the LAMOST survey directly using their spectral line features. The completeness of the identification is about 75% for tests based on LAMOST stellar parameters. The contamination in the identified K giant sample is lower than 2.5%. Applying the classification method to about 2 million LAMOST spectra observed during the pilot survey and the first year survey, we select 298,036 K giant candidates. The metallicities of the sample are also estimated with uncertainty of 0.13 ∼ 0.29 dex based on the equivalent widths of Mg b and iron lines. A Bayesian method is then developed to estimate the posterior probability of the distance for the K giant stars, based on the estimated metallicity and 2MASS photometry. The synthetic isochrone-based distance estimates have been calibrated using 7 globular clusters with a wide range of metallicities. The uncertainty of the estimated distance modulus at K = 11 mag, which is the median brightness of the K giant sample, is about 0.6 mag, corresponding to ∼ 30% in distance. As a scientific verification case, the trailing arm of the Sagittarius stream is clearly identified with the selected K giant sample. Moreover, at about 80 kpc from the Sun, we use our K giant stars to confirm a detection of stream members near the apo-center of the trailing tail. These rediscoveries of the features of the Sagittarius stream illustrate the potential of the LAMOST survey for detecting substructures in the halo of the Milky Way.

show abstract

Section: Lamost Survey Datamentioning

confidence: 99%

The K Giant Stars From the Lamost Survey Data. I. Identification, Metallicity, and Distance

Liu

Deng

Carlin

et al. 2014

ApJ

Self Cite

View full text Add to dashboard Cite

show abstract

“…Various methods have been developed in the past to derive stellar atmospheric parameters from large number of mediumto-low resolution spectra (Recio-Blanco et al 2006;Lee et al 2008a, Wu et al 2011. The approaches generally fall into two main categories of method (Wu et al 2011): the minimum distance method (MDM) and non-linear regression method.…”

Section: Introductionmentioning

confidence: 99%

“…The approaches generally fall into two main categories of method (Wu et al 2011): the minimum distance method (MDM) and non-linear regression method. Both categories of method have been applied to large stellar spectroscopic surveys, including the SEGUE (Yanny et al 2009), RAVE (Steinmetz et al 2006), APOGEE (Majewski et al 2007) and LAMOST ).…”

Section: Introductionmentioning

confidence: 99%

“…The MDM is usually based on spectral template matching, and searches for the template spectrum that has the shortest distance in parameter space from the target spectrum. The χ 2 minimization, cross-correlation, weighted mean algorithm, and the k-nearest neighbor (KNN), are thought to be specific cases of MDM (Wu et al 2011). Softwares and pipelines developed based on those algorithms include the TGMET (Katz et al 1998), MA-TISSE (Recio-Blanco et al 2006), SSPP (Lee et al 2008a), ULySS (Koleva et al 2009), that of Allende Prieto et al (2006) and of Zwitter et al (2008).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The LAMOST stellar parameter pipeline at Peking University – lsp3

Xiang

Liu

Yuan

et al. 2015

Monthly Notices of the Royal Astronomical Society

150

View full text Add to dashboard Cite

We introduce the LAMOST Stellar Parameter Pipeline at Peking University -LSP3, developed and implemented for the determinations of radial velocity V r and stellar atmospheric parameters (effective temperature T eff , surface gravity log g, metallicity [Fe/H]) for the LAM-OST Spectroscopic Survey of the Galactic Anti-center (LSS-GAC). We describe the algorithms of LSP3 and examine the accuracy of parameters yielded by it. The precision and accuracy of parameters yielded are investigated by comparing results of multi-epoch observations and of candidate members of open and globular clusters, with photometric calibration, as well as with independent determinations available from a number of external databases, including the PASTEL archive, the APOGEE, SDSS and RAVE surveys, as well as those released in the LAMOST DR1. The uncertainties of LSP3 parameters are characterized and quantified as a function of the spectral signal-to-noise ratio (SNR) and stellar atmospheric parameters. We conclude that the current implementation of LSP3 has achieved an accuracy of 5.0 km s −1 , 150 K, 0.25 dex, 0.15 dex for the radial velocity, effective temperature, surface gravity and metallicity, respectively, for LSS-GAC spectra of FGK stars of SNRs per pixel higher than 10. The LSP3 has been applied to over a million LSS-GAC spectra collected hitherto. Stellar parameters yielded by the LSP3 will be released to the general public following the data policy of LAMOST, together with estimates of the interstellar extinction E(B − V ) and stellar distances, deduced by combining spectroscopic and multi-band photometric measurements using a variety of techniques.

show abstract

“…To extract stellar parameters from such a large number of spectra, an automated stellar parameter pipeline -ULySS was developed (Wu et al, 2011). There are 1,944,406 spectra of stars in DR1 catalogs; 1,085,404 of them yield a full set of stellar atmospheric parameters and radial velocities due to quality control.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of Lamost Dr1 Stellar Parameters

Gao¹,

Zhang²,

Xiang³

et al. 2015

Publications of The Korean Astronomical Society

Self Cite

View full text Add to dashboard Cite

We adopt the PASTEL catalog combined with SIMBAD radial velocities as a testing standard to validate the stellar parameters (effective temperature T eff , surface gravity log g, metallicity [Fe/H] and radial velocity V r ) from the first data release (DR1) of The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey. After applying data reduction and temperature constraints to the sample obtained by cross-identification, we compare the stellar parameters from DR1 and PASTEL. The results show that the DR1 results are reliable under certain conditions. We derive a dispersion of 110 K, 0.19 dex, 0.11 dex and 4.91 km s −1 in specified effective temperature ranges, for T eff , log g, [Fe/H] and V r respectively. Systematic errors are negligible except for those of V r . In addition, for stars with PASTEL [Fe/H] < −1.5, the metallicities in DR1 are systematically higher than those in PASTEL.

show abstract

Automatic determination of stellar atmospheric parameters and construction of stellar spectral templates of the Guoshoujing Telescope (LAMOST)

Cited by 225 publications

References 87 publications

The K Giant Stars From the Lamost Survey Data. I. Identification, Metallicity, and Distance

The K Giant Stars From the Lamost Survey Data. I. Identification, Metallicity, and Distance

The LAMOST stellar parameter pipeline at Peking University – lsp3

Accuracy of Lamost Dr1 Stellar Parameters

Contact Info

Product

Resources

About