Related papers: Label Transfer from APOGEE to LAMOST: Precise Stel…
We explore the application of artificial neural networks (ANNs) for the estimation of atmospheric parameters (Teff, logg, and [Fe/H]) for Galactic F- and G-type stars. The ANNs are fed with medium-resolution (~ 1-2 A) non flux-calibrated…
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…
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) 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 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…
The LAMOST-\textit{K}2 (L\textit{K}2) project, initiated in 2015, aims to collect low-resolution spectra of targets in the \textit{K}2 campaigns, similar to LAMOST-\textit{Kepler} project. By the end of 2018, a total of 126 L\textit{K}2…
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) project performed its five year formal survey since Sep. 2012, already fulfilled the pilot survey and the 1st two years general survey with an output - spectroscopic…
Low-resolution spectra are proved competitive to high-resolution spectra in determining many stellar labels at comparable precision. It is useful to consider the spectral information content when assessing the capability of a stellar…
The cool temperatures of M dwarf atmospheres enable complex molecular chemistry, making robust characterization of M dwarf compositions a long-standing challenge. Recent modifications to spectral synthesis pipelines have enabled more…
All of the 14 subfields of the Kepler field have been observed at least once with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST, Xinglong Observatory, China) during the 2012-2014 observation seasons. There are 88,628…
We present a method to estimate distances to stars with spectroscopically derived stellar parameters. The technique is a Bayesian approach with likelihood estimated via comparison of measured parameters to a grid of stellar isochrones, and…
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…
Spectroscopic surveys require fast and efficient analysis methods to maximize their scientific impact. Here we apply a deep neural network architecture to analyze both SDSS-III APOGEE DR13 and synthetic stellar spectra. When our…
We present an empirical stellar spectra library created using spectra from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) DR5. This library represents a uniform data set ranging from 3750 through 8500K in effective…
Analysing stellar parameters and abundances from nearly one million Gaia Data Release 3 (DR3) Radial Velocity Spectrometer (RVS) spectra poses challenges due to the limited spectral coverage (restricted to the infrared Ca II triplet) and…
Large photometric surveys will image billions of galaxies, but we currently lack quick, reliable automated ways to infer their physical properties like morphology, stellar mass, and star formation rates. Simulations provide galaxy images…
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…
High-precision stellar mass and radius measured directly from binaries can effectively calibrate the stellar models. However, such a database containing full spectral types and large range of metallicity is still not fully established. A…
We investigate applications of machine learning models to directly infer physical properties of brown dwarfs from their photometry and spectra using $\textit{The Cannon}$. We demonstrate that absolute magnitudes, spectral types, and…
Early-type stars are key drivers of Galactic chemical evolution, enriching the interstellar medium with alpha elements through powerful stellar winds and core-collapse supernovae, fueled by their short lifetimes and high masses. However,…