Speaker
Description
Over 70% of stars in the local solar neighbourhood are M-dwarfs, and they are frequent hosts of exoplanets. However, their cool, complex atmospheres and intrinsic faintness present significant observational and modelling challenges. The upcoming PLATO mission is expected to observe over 5000 early- to mid-type M-dwarfs, providing high-precision light curves for exoplanet detection, along with extensive spectroscopic follow-up from ground-based facilities. We present a fast and reliable pipeline for deriving homogeneous stellar parameters and chemical abundances by combining photometric and spectroscopic data within a Bayesian framework. The spectroscopic module uses an artificial neural network (ANN) to determine effective temperatures, metallicity and abundances from H-band spectra, with log g being constrained by the photometric module to break degeneracy. We discuss the pipeline’s performance, recent and planned improvements, and validation using benchmark stars. The resulting catalogue of homogeneous M-dwarfs parameters from this pipeline will provide a vital foundation for PLATO’s stellar and exoplanet science for low-mass stars.