Reliable stellar abundances of individual stars with the MUSE
  integral-field spectrograph
  We present a novel approach to deriving stellar labels for stars observed in
MUSE fields making use of data-driven machine learning methods. Taking
advantage of the comparable spectral properties (resolution, wavelength
coverage) of the LAMOST and MUSE instruments, we adopt the Data-Driven Payne
(DD-Payne) model used on LAMOST observations and apply it to stars observed in
MUSE fields. Remarkably, in spite of instrumental differences, we are able to
determine stellar labels to better than 70K in $T_{\rm eff}$, 0.15 dex in $\log
g$, and 0.1 dex in several abundances ([Fe/H], [Mg/Fe], [Si/Fe], etc) for
current MUSE observations. To date, MUSE has been used to target 13,000 fields
across the southern sky since it was first commissioned six years ago and it is
unique in its ability to study dense star fields such as globular clusters or
the Milky Way bulge. Our method will enable the automated determination of
stellar parameters for all stars in these fields. Additionally, it opens the
door for applications to data collected by other spectrographs having
resolution similar to LAMOST. With the upcoming BlueMUSE and MAVIS, we will
gain access to a whole new range of chemical abundances with higher precision,
especially critical s-process elements such as [Y/Fe] and [Ba/Fe] that provide
key age diagnostics for stellar targets.
