CHESS is a pipeline for the large scale analysis of stellar spectroscopic data. It's goal is to enable the
extraction of complete, precise, and accurate chemical abundances from large samples of stellar spectra. CHESS relies
on a combination of the physical modelling of stellar spectra with machine learning methods.
This project is going to provide multi-elemental stellar chemical abundances of unprecedented quality for a sample of more than
10 000 F-, G-, or K-type stars observed with the UVES spectrograph (Ultraviolet and Visual Echelle Spectrograph). The project aims
to reveal the sequence of events that describe the chemical evolution of the Galaxy from the early stages to the present. By playing
(running) CHESS with (a large sample of) stars, a quality jump in the determination of stellar chemical abundances will be achieved and,
as consequence, we will take the understanding of the Galactic chemical enrichment to whole new level.