Related papers: Label Transfer from APOGEE to LAMOST: Precise Stel…

The LAMOST survey has acquired low-resolution spectra (R=1,800) for 5 million stars across the Milky Way, far more than any current stellar survey at a corresponding or higher spectral resolution. It is often assumed that only very few…

We present a novel approach to deriving stellar labels for stars observed in MUSE fields making use of data-driven machine learning methods. Taking advantage of the comparable spectral properties (resolution, wavelength coverage) of the…

The mass of a star is arguably its most fundamental parameter. For red giant stars, tracers luminous enough to be observed across the Galaxy, mass implies a stellar evolution age. It has proven to be extremely difficult to infer ages and…

A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated…

APOGEE and GALAH are two high resolution multi-object spectroscopic surveys that provide fundamental stellar parameters and multiple elemental abundance estimates for about half a million stars in the Milky Way. Both surveys observe in…

We derive stellar parameters and abundances (`stellar labels') of 40,034 late-B and A-type main-sequence stars extracted from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope Medium Resolution Survey (LAMOST--MRS). The primary…

Recently the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) published its first data release (DR1), which is ranked as the largest stellar spectra dataset in the world so far. We combine the PASTEL catalog and SIMBAD…

Precise stellar parameters for M-type stars, the Galaxy's most common stellar type, are crucial for numerous studies. In this work, we refined the LAMOST DR10 M-type star catalog through a two-stage process. First, we purified the catalog…

In this study, the fundamental stellar atmospheric parameters (Teff, log g, [Fe/H] and [{\alpha}/Fe]) were derived for low-resolution spectroscopy from LAMOST DR5 with Generative Spectrum Networks (GSN). This follows the same scheme as a…

We present The Payne, a general method for the precise and simultaneous determination of numerous stellar labels from observed spectra, based on fitting physical spectral models. The Payne combines a number of important methodological…

Estimating precise metallicity of M dwarfs is a well-known difficult problem due to their complex spectra. In this work, we empirically calibrate the metallicity using wide binaries with a F, G, or K dwarf and a M dwarf companion. With 1308…

We present a new analysis of the LAMOST DR1 survey spectral database performed with the code SP_Ace, which provides the derived stellar parameters T$_{\rm eff}$, log (g), [Fe/H], and [$\alpha$/Fe] for 1,097,231 stellar objects. We tested…

We have compared the stellar parameters, temperature, gravity and metallicity, between the LAMOST-DR2 and SDSS-DR12/APOGEE database for stars in common. It is found that the LAMOST database provides a better red-clump feature than the…

Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to…

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…

Large-scale spectroscopic surveys have collectively observed millions of stars across the Milky Way, but each derives stellar labels using independent pipelines with distinct modelling assumptions, introducing systematic offsets that…

Stellar parameters for large samples of stars play a crucial role in constraining the nature of stars and stellar populations in the Galaxy. An increasing number of medium-band photometric surveys are presently used in estimating stellar…

Machine learning allows efficient extraction of physical properties from stellar spectra that have been obtained by large surveys. The viability of ML approaches has been demonstrated for spectra covering a variety of wavelengths and…

We present the determination of stellar parameters and individual elemental abundances for 6 million stars from $\sim$8 million low-resolution ($R\sim1800$) spectra from LAMOST DR5. This is based on a modeling approach that we dub $The$…

High-precision photometric standard stars play a key role in enabling accurate photometric calibration and advancing various fields of astronomy. However, due to limitations in calibration methods and the limited availability and underuse…