The Milky Way is a puzzle made of hundreds of billions of individual pieces: a spectacular mixture of stars of all ages, some newly born and some as old as the Universe itself. With the combined data from telescopes on the ground and in space we determine the positions, motions, and chemical composition of millions of stars to reconstruct our Galaxy’s formation history. How many smaller galaxies have been cannibalized by the beautiful large spiral galaxy we live in today? The starlight also reveals the origin of elements; how much the stars synthesize and how much they destroy. Was the Li in your battery created in the Big Bang itself? Which violent stellar explosions created the Cu in your saucepan?
To find the answers to these and other questions requires development of instruments, analysis methods, and theory. Central to this ERC project are three optical multi-object spectrographs, each connected to a 4m-telescope: GALAH@AAT (Siding Spring Observatory, Australia), WEAVE@WHT (Roque de los Muchachos Observatory, La Palma, Spain) and 4MOST@VISTA (Paranal Observatory, Chile). Together, the surveys will collect tens of millions of stellar spectra, a regime change that can only be mastered by introducing elements of machine learning in the analysis and scientific exploitation.
Of fundamental importance is also the quality of synthetic stellar spectra, which we generate by computing a model of the stellar atmosphere from where light is emitted. If the model is based on erroneous physical assumptions or uncertain atomic data, we will infer inaccurate chemical abundances and fundamental stellar parameters. In this ERC project, we abandon the traditional modelling assumptions that stellar atmospheres are static and one-dimensional and that all matter and radiation are in equilibrium (LTE), which leads to improvements that are particularly significant for the oldest and most chemically pristine (metal-poor) stars in our Galaxy.