Data-Driven Stellar Models
  We develop a data-driven model to map stellar parameters (effective
temperature, surface gravity and metallicity) accurately and precisely to
broad-band stellar photometry. This model must, and does, simultaneously
constrain the passband-specific dust reddening vector in the Milky Way. 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 dataset with
spectroscopic parameters from LAMOST, APOGEE and GALAH, Gaia parallaxes, and
optical and near-infrared photometry from Gaia, Pan-STARRS~1, 2MASS and WISE.
Testing the model on these datasets 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,
stellar-temperature-dependent reddening vector. 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 may be obtained at
https://doi.org/10.5281/zenodo.3902382.
