The Payne: self-consistent ab initio fitting of stellar spectra
  We present The Payne, a general method for the precise and simultaneous
determination of numerous stellar labels from observed spectra, based on
fitting physical spectral models. The Payne combines a number of important
methodological aspects: it exploits the information from much of the available
spectral range; it fits all labels (stellar parameters and element abundances)
simultaneously; it uses spectral models, where the atmosphere structure and the
radiative transport are consistently calculated to reflect the stellar labels.
At its core The Payne has an approach to accurate and precise interpolation and
prediction of the spectrum in high-dimensional label-space, which is flexible
and robust, yet based on only a moderate number of ab initio models (O(1000)
for 25 labels). With a simple neural-net-like functional form and a suitable
choice of training labels, this interpolation yields a spectral flux prediction
good to $10^{-3}$ rms across a wide range of $T_{\rm eff}$ and log g (including
dwarfs and giants). We illustrate the power of this approach by applying it to
the APOGEE DR14 data set, drawing on Kurucz models with recently improved line
lists: without recalibration, we obtain physically sensible stellar parameters
as well as 15 element abundances that appear to be more precise than the
published APOGEE DR14 values. In short, The Payne is an approach that for the
first time combines all these key ingredients, necessary for progress towards
optimal modelling of survey spectra; and it leads to both precise and accurate
estimates of stellar labels, based on physical models and without
re-calibration. Both the codes and catalog are made publicly available online.
