  In this era of large-scale stellar spectroscopic surveys, measurements of
stellar attributes ("labels," i.e. parameters and abundances) must be made
precise and consistent across surveys. Here, we demonstrate that this can be
achieved by a data-driven approach to spectral modeling. With The Cannon, we
transfer information from the APOGEE survey to determine precise Teff, log g,
[Fe/H], and [$\alpha$/M] from the spectra of 450,000 LAMOST giants. The Cannon
fits a predictive model for LAMOST spectra using 9952 stars observed in common
between the two surveys, taking five labels from APOGEE DR12 as ground truth:
Teff, log g, [Fe/H], [\alpha/M], and K-band extinction $A_k$. The model is then
used to infer Teff, log g, [Fe/H], and [$\alpha$/M] for 454,180 giants, 20% of
the LAMOST DR2 stellar sample. These are the first [$\alpha$/M] values for the
full set of LAMOST giants, and the largest catalog of [$\alpha$/M] for giant
stars to date. Furthermore, these labels are by construction on the APOGEE
label scale; for spectra with S/N > 50, cross-validation of the model yields
typical uncertainties of 70K in Teff, 0.1 in log g, 0.1 in [Fe/H], and 0.04 in
[$\alpha$/M], values comparable to the broadly stated, conservative APOGEE DR12
uncertainties. Thus, by using "label transfer" to tie low-resolution (LAMOST R
$\sim$ 1800) spectra to the label scale of a much higher-resolution (APOGEE R
$\sim$ 22,500) survey, we substantially reduce the inconsistencies between
labels measured by the individual survey pipelines. This demonstrates that
label transfer with The Cannon can successfully bring different surveys onto
the same physical scale.
