An accurate and self-consistent chemical abundance catalogue for the APOGEE/Kepler sample
Context. The APOGEE survey has obtained high-resolution infrared spectra of more than 100,000 stars. Deriving chemical abun- dances patterns of these stars is paramount to piecing together the structure of the Milky Way. While the derived chemical abundances have been shown to be precise for most stars, some calibration problems have been reported, in particular for more metal-poor stars. Aims. In this paper, we aim to (1) re-determine the chemical abundances of the APOGEE + Kepler stellar sample (APOKASC) with an independent procedure, line list and line selection, and high-quality surface gravity information from asteroseismology, and (2) extend the abundance catalogue by including abundances that are not currently reported in the most recent APOGEE release (DR12). Methods. We fixed the T e ff and log g to those determined using spectrophotometric and asteroseismic techniques, respectively. We made use of the Brussels Automatic Stellar Parameter (BACCHUS) code to derive the metallicity and broadening parameters for the APOKASC sample. In addition, we derived di ff erential abundances with respect to Arcturus. Results. We have validated the BACCHUS code on APOGEE data using several well-known stars, and stars from open and globular clusters. We also provide the abundances of C, N, O, Mg, Ca, Si, Ti, S, Al, Na, Ni, Mn, Fe, K, and V for every star and line, and show the impact of line selection on the final abundances. Improvements have been made for some elements (e.g. Ti, Si, V). Additionally, we measure new abundance ratios not found in the current APOGEE release including P, Cu, Rb, and Yb, which are only upper limits at this time, as well as Co and Cr which are promising. Conclusions. In this paper, we present an independent analysis of the APOKASC sample and provide abundances of up to 21 elements. This catalogue can be used not only to study chemical abundance patterns of the Galaxy but also to train data driven spectral approaches which can improve the abundance precision in a restricted dataset, but also full APOGEE sample.