.png){kind=icon}
Return
Data-driven Stellar Models
.png){kind=icon}
OA
AI
DOI：10.3847/1538-4357/abd1dd.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We developed a data-driven model to map stellar parameters (T-eff, log g, and [Fe/H]) accurately and precisely to broadband stellar photometry. This model must, and does, simultaneously constrain the passband-specific dust reddening vector in the Milky Way, R. The model uses a neural network to learn the (de-reddened) absolute magnitude in one band and colors across many bands, given stellar parameters from spectroscopic surveys and parallax constraints from Gaia. To demonstrate the effectiveness of this approach, we train our model on a data set with spectroscopic parameters from LAMOST, APOGEE, and GALAH, Gaia parallaxes, and optical and near-infrared photometry from Gaia, Pan-STARRS 1, Two Micron All Sky Survey and Wide-field Infrared Survey Explorer. Testing the model on these data sets leads to an excellent fit and a precise-and by construction-accurate prediction of the color-magnitude diagrams in many bands. This flexible approach rigorously links spectroscopic and photometric surveys, and also results in an improved, T-eff-dependent R. As such, it provides a simple and accurate method for predicting photometry in stellar evolutionary models. Our model will form a basis to infer stellar properties, distances, and dust extinction from photometric data, which should be of great use in 3D mapping of the Milky Way. Our trained model can be obtained at doi:10.5281/zenodo.3902382.
Keywords:
Astrostatistics
Neural networks
Stellar photometry
Interstellar dust extinction
Journal
IF:
5.4
Papers:
8.3W
Citations:
32.0W
Organization
Cited Papers
ShareSaveShareSaveShareSaveShareSaveSpectral irradiance calibration in the infrared. XIV. The absolute calibration of 2MASS.png)
ASTRONOMICAL JOURNAL
OAAI
ASTRONOMICAL JOURNALIF5.1
OAAIShareSaveShareSave
View more