Understanding the formation of the Milky Way disk

• Applicant: Dr. Ivan Minchev, Ph.D.
• Subject Area: Astrophysics and Astronomy
• Term: from 2015 to 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 273216187

In the next decade large Milky Way surveys, most notably the recently launched Gaia space mission, will provide precise positions, stellar parameters and kinematics for up to 1 billion stars. Such an observational database will have the precision necessary to unravel the structure, formation and evolution of our Galaxy in detail, vastly surpassing our knowledge of other galaxies. However, to be able to analyze and interpret the large amounts of extremely precise forthcoming data, we need drastically improved models. The increasing complexity of substructure found in the phase-space of Milky Way stars calls for sophisticated numerical modeling, where disk asymmetries (e.g., gravitational perturbations from the bar, spirals, and merging satellites) and non-equilibrium processes are taken into account self-consistently. Because stars move away from their birthplaces (i.e., migrate radially), incorporating chemical information in the models is crucial for recovering the Milky Way evolutionary history. The reason for that is that the chemical composition of stellar atmospheres is preserved throughout their lifetimes, which can give us clues about when and where stars were born. I propose a program that will develop Galactic disk models addressing all of the above problems,. This research will be hosted by the Leibniz-Institut für Astrophysik (AIP), a leader of the Gaia follow-up facility 4MOST and a member of most major Galactic surveys (RAVE, SEGUE, APOGEE, Gaia-ESO). We will provide tests for different Milky Way evolutionary scenarios by using a novel technique, which combines sophisticated chemical evolution models able to trace a large number of chemical elements with state-of-the-art cosmological simulations. The ultimate goal of this project is to constrain the Milky Way assembly history, taking full advantage of currently available and forthcoming observational data and our models.

• DFG Programme: Research Grants