Objective
Our Milky Way is a very typical galaxy, yet our position within it makes it a unique object of study: star-by-star we can obtain 3D positions, 3D velocities and chemical abundances. This wealth of information about our Galaxy’s stellar body holds the key to understanding how disk galaxies form and how dark matter acts on the scales of galaxies. Ongoing surveys have recently hundred-folded the number of stars with good distance estimates, radial and transverse velocities and abundance estimates; and this only forebodes the data wealth expected from ESA’s Gaia mission. Yet, practical approaches to extract the enormous astrophysical information content of these data have been sorely underdeveloped. Just within the last year, the PI and his collaborators have pioneered how to construct rigorously both mass density and kinematic maps of the Milky Way's stellar disk from existing spectroscopic surveys. It is proposed here to unleash the full potential of this approach.
This proposal builds on the PI's unique track record encompassing both extensive survey data analysis and detailed dynamical modeling, combined with his role in proprietary key data sets for this project. Focusing a group of experienced post-docs and PhD students for five years on these challenges will bring the critical mass in one single place to implementing such a comprehensive data/modeling machinery. The first years of the project will focus on the technique development and applications to ground-based data. The results will tell us how the Galaxy's disk was built and shaped, and will map dark matter in the inner parts of the MW. Such an analysis machinery is also indispensable for capitalizing (astrophysically) on the catalogs that the Gaia mission will provide.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- natural sciencescomputer and information sciencesdata science
- natural sciencesphysical sciencesastronomyastrophysicsdark matter
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Call for proposal
ERC-2012-ADG_20120216
See other projects for this call
Funding Scheme
ERC-AG - ERC Advanced GrantHost institution
80539 Munchen
Germany