2022MNRAS.514.1034W

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 and 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, according to the cross-validation of 27 LAMOST-MUSE common stars, we are able to determine stellar labels with precision better than 75K in T_eff, 0.15 dex in log g, and 0.1 dex in abundances of [Fe/H], [Mg/Fe], [Si/Fe], [Ti/Fe], [C/Fe], [Ni/Fe], and [Cr/Fe] for current MUSE observations over a parameter range of 3800 < T_eff < 7000 K, -1.5 < [Fe/H] < 0.5 dex. To date, MUSE has been used to target 13 000 fields across the southern sky since it was first commissioned 6 yr 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.