Related papers: Label Transfer from APOGEE to LAMOST: Precise Stel…
New spectroscopic surveys offer the promise of consistent stellar parameters and abundances ('stellar labels') for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge. In many cases, there is…
We measure carbon and nitrogen abundances to $\lesssim$ 0.1 dex for 450,000 giant stars from their low-resolution (R$\sim$1800) LAMOST DR2 survey spectra. We use these [C/M] and [N/M] measurements, together with empirical relations based on…
We have shown that data-driven models are effective for inferring physical attributes of stars (labels; Teff, logg, [M/H]) from spectra, even when the signal-to-noise ratio is low. Here we explore whether this is possible when the…
We set out to determine stellar labels from low-resolution survey spectra of hot, OBA stars with effective temperature (Teff) higher than 7500K. This fills a gap in the scientific analysis of large spectroscopic stellar surveys such as…
M dwarfs have enormous potential for our understanding of structure and formation on both Galactic and exoplanetary scales through their properties and compositions. However, current atmosphere models have limited ability to reproduce…
We present fundamental atmospheric parameters (Teff and log g) and metallicities ([M/H]) for 507,513 M dwarf stars using low-resolution spectra (R~1800) from LAMOST DR10. By employing Cycle-StarNet, an innovative domain adaptation approach,…
The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) has obtained more than 23 million spectra, opening an unprecedented opportunity to study stellar physics, as well as the formation and evolution of our Milky Way. In…
We undertake a critical and comprehensive comparison of the radial velocities and the main stellar atmospheric parameters for stars in common between the latest data releases from the APOGEE and the LAMOST surveys. There is a total of…
The advent of large-scale spectroscopic surveys underscores the need to develop robust techniques for determining stellar properties ("labels", i.e., physical parameters and elemental abundances). However, traditional spectroscopic methods…
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…
We use the data-driven method, The Cannon, to bring 21,000 stars from the ARGOS bulge survey, including 10,000 red clump stars, onto the parameter and abundance scales of the cross-Galactic survey, APOGEE, obtaining rms precisions of 0.10…
Asteroseismology is a powerful tool to precisely determine the evolutionary status and fundamental properties of stars. With the unprecedented precision and nearly continuous photometric data acquired by the NASA Kepler mission, parameters…
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…
Precise spectroscopic classification of planet hosts is an important tool of exoplanet research at both the population and individual system level. In the era of large-scale surveys, data-driven methods offer an efficient approach to…
M dwarfs are the most common type of star in the Galaxy, and because of their small size are favored targets for searches of Earth-sized transiting exoplanets. Current and upcoming all-sky spectroscopic surveys, such as the Large Sky Area…
Large stellar surveys are revealing the chemodynamical structure of the Galaxy across a vast spatial extent. However, the many millions of low-resolution spectra observed to date are yet to be fully exploited. We employ The Cannon, a…
M dwarf stars are the most common stars in the Galaxy, dominating the population of the Galaxy by numbers at faint magnitudes. Precise and accurate stellar parameters for M dwarfs are of crucial importance for many studies. However, the…
To accurately interpret the observed properties of exoplanets, it is necessary to first obtain a detailed understanding of host star properties. However, physical models that analyze stellar properties on a per-star basis can become…
Chemical tagging promises to use detailed abundance measurements to identify spatially separated stars that were in fact born together (in the same molecular cloud), long ago. This idea has not yielded much practical success, presumably…
As a typical data-driven method, deep learning becomes a natural choice for analysing astronomical data nowadays. In this study, we built a deep convolutional neural network to estimate basic stellar parameters $T\rm{_{eff}}$, log g,…