Related papers: The stellar parameters and elemental abundances fr…
Stellar parameters and abundances provide crucial insights into stellar and Galactic evolution studies. In this work, we developed a convolutional neural network (CNN) to estimate stellar parameters: effective temperature…
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 $The$…
Deep learning with artificial neural networks is increasingly gaining attention, because of its potential for data-driven astronomy. However, this methodology usually does not provide uncertainties and does not deal with incompleteness and…
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…
Accurate determination of stellar atmospheric parameters and elemental abundances is crucial for Galactic archeology via large-scale spectroscopic surveys. In this paper, we estimate stellar atmospheric parameters -- effective temperature…
Deriving stellar atmospheric parameters and chemical abundances from stellar spectra is crucial for understanding the evolution of the Milky Way. By performing a fitting with MARCS model atmospheric theoretical synthetic spectra combined…
The fundamental stellar atmospheric parameters T_eff and log g and 13 chemical abundances are derived for medium-resolution spectroscopy from LAMOST Medium-Resolution Survey (MRS) data sets with a deep-learning method. The neural networks…
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has acquired tens of millions of low-resolution spectra of stars. This paper investigated the parameter estimation problem for these spectra. To this end, we proposed a…
We present a value-added catalog containing stellar parameters estimated from 7.10 million low-resolution spectra for 5.16 million unique stars with spectral signal-to-noise ratios (SNRs) higher than 10 obtained by the Large Sky Area…
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…
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$<$30. Therefore, it…
A deep understanding of our Galaxy desires detailed decomposition of its stellar populations via their chemical fingerprints. This requires precise stellar abundances of many elements for a large number of stars. Here we present an updated…
In this study, the fundamental stellar atmospheric parameters (Teff, log g, [Fe/H] and [{\alpha}/Fe]) were derived for low-resolution spectroscopy from LAMOST DR5 with Generative Spectrum Networks (GSN). This follows the same scheme as a…
We present a new analysis of the LAMOST DR1 survey spectral database performed with the code SP_Ace, which provides the derived stellar parameters T$_{\rm eff}$, log (g), [Fe/H], and [$\alpha$/Fe] for 1,097,231 stellar objects. We tested…
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…
This paper investigates the problem of estimating three stellar atmospheric physical parameters and thirteen elemental abundances for medium-resolution spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). Typical…
Since Gaia DR2 was released, many velocity structures in the disk have been revealed such as large scale ridge-like patterns in the phase space. Both kinematic information and stellar elemental abundances are needed to reveal their…
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) Low Resolution Spectroscopic Survey (LRS) provides massive spectroscopic data of M-type stars, and the derived stellar parameters could bring vital help to various…
We train a convolutional neural network, APOGEE Net, to predict $T_\mathrm{eff}$, $\log g$, and, for some stars, [Fe/H], based on the APOGEE spectra. This is the first pipeline adapted for these data that is capable of estimating these…
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…