Deriving the stellar labels of LAMOST spectra with Stellar LAbel Machine (SLAM)

Understand

The LAMOST survey has provided 9 million spectra in its Data Release 5 (DR5) at R$\sim$1800.

• Extracting precise stellar labels is crucial for such a large sample.
• In this paper, we report the implementation of the Stellar LAbel Machine (SLAM), which is a data-driven method based on Support Vector Regression (SVR), a robust non-linear regression technique.
• Thanks to the capability to model highly non-linear problems with SVR, SLAM generally can derive stellar labels over a wide range of spectral types.