相关论文: Data-Driven Stellar Models

We pose the question of how much information on the atmospheric parameters of late-type stars can be retrieved purely from colors using standard photometric systems. We carried out numerical experiments using stellar fluxes from model…

Broadband photometry of galaxies measures an unresolved mix of complex stellar populations, gas, and dust. Interpreting these data is a challenge for models: many studies have shown that properties derived from modeling galaxy photometry…

We present a new technique based on multi-band near ultraviolet and optical photometry to measure both the stellar intrinsic properties, ie luminosity and effective temperature, and the interstellar dust extinction along the line of sight…

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…

Stellar variability is driven by a multitude of internal physical processes that depend on fundamental stellar properties. These properties are our bridge to reconciling stellar observations with stellar physics, and for understanding the…

Data-driven stellar classification has a long and important history in astronomy, dating as far back as Annie Jump Cannon's "by eye" classifications of stars into spectral types still used today. In recent years, data-driven spectroscopy…

We present a method to infer reddenings and distances to stars, based only on their broad-band photometry, and show how this method can be used to produce a three-dimensional dust map of the Galaxy. Our method samples from the full…

We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first…

Stellar physical and dynamical properties are essential knowledge to understanding the structure, formation, and evolution of our Galaxy. We produced an all-sky uniformly derived catalog of stellar astrophysical parameters (APs; age, mass,…

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…

Deriving atmospheric parameters of a large sample of stars is of vital importance to understand the formation and evolution of the Milky Way. Photometric surveys, especially those with near-ultraviolet filters, can offer accurate…

With contemporary infrared spectroscopic surveys like APOGEE, red-giant stars can be observed to distances and extinctions at which Gaia parallaxes are not highly informative. Yet the combination of effective temperature, surface gravity,…

Red clump stars (RCs) are useful tracers of distances, extinction, chemical abundances, and Galactic structures and kinematics. Accurate estimation of the RC parameters -- absolute magnitude and intrinsic color -- is the basis for obtaining…

We present a machine learning method to assign stellar parameters (temperature, surface gravity, metallicity) to the photometric data of large photometric surveys such as SDSS and SKYMAPPER. The method makes use of our previous effort in…

Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…

Accurate determinations of atmospheric parameters (effective temperature $T_{\rm eff}$, surface gravity log $g$ and metallicity [Fe/H]) and distances for large complete samples are of vital importance for various Galactic studies. We have…

We present a method to simultaneously infer the interstellar extinction parameters $A_0$ and $R_0$, stellar effective temperature $T_{\rm eff}$, and distance modulus $\mu$ in a Bayesian framework. Using multi-band photometry from SDSS and…

Intrinsic colors (ICs) of stars are essential for the studies on both stellar physics and dust reddening. In this work, we developed an XGBoost model to predict the ICs with the atmospheric parameters $T_{\rm eff}$, ${\rm log}\,g$, and $\rm…

The determination of atmospheric parameters is the first and most fundamental step in the analysis of a stellar spectrum. Current and forthcoming surveys involve samples of up to several million stars, and therefore fully automated…

We present a new three-dimensional map of dust reddening, based on Gaia parallaxes and stellar photometry from Pan-STARRS 1 and 2MASS. This map covers the sky north of a declination of -30 degrees, out to a distance of several kiloparsecs.…