Astronomers are using the chemical fingerprints of individual stars to map the hidden structure and dark matter of the Milky Way. The problem is that we do not know how much dark matter our galaxy contains, or exactly how it is distributed. Traditional methods for measuring the galaxy’s mass rely on tracking star motions, but these techniques are easily skewed by the way surveys select which stars to observe—a bias that is hard to correct. This project sidesteps that problem by using element abundances, which are less sensitive to such selection effects. The team will combine data from the Gaia satellite and large spectroscopic surveys, then apply a technique called Orbital Torus Imaging to build a data-driven model of the galaxy’s spatial, orbital, and mass distribution. If successful, the research will produce the first robust, selection-function-free measurement of dark matter across the Milky Way. It will also reveal how much the galaxy is out of equilibrium—whether it is still settling from past collisions. This is fundamental science with no immediate practical application. But understanding how galaxies like ours assemble and hold together is essential context for every model of cosmic structure, and past work on galactic dynamics has underpinned everything from satellite navigation to tests of general relativity.
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The Milky Way is currently the only galaxy resolvable on a star-by-star basis with large statistical samples, making it the perfect testbed for exploring the structure of galaxies. Some of the pivotal open questions that still remain unanswered regarding galactic structure are: how much mass and dark matter do galaxies contain, and how is it spatially distributed? Under the assumption of a simple and time-invariant potential, many galactic dynamics techniques (e.g., Jeans or Schwarzchild models) infer the Milky Way's mass and dark matter content and distribution from stellar kinematic observations. These methods typically rely on parameterised potential models of the Milky Way and must take into account non-trivial survey selection effects, because they are making use of the density of stars in phase space. Large-scale spectroscopic surveys now supply information beyond kinematics in the form of precise stellar label measurements (especially element abundances). These element abundances are known to correlate with orbital actions or other dynamical invariants, and in many cases can be measured without detailed knowledge of the stellar selection function, therefore making element abundance gradients less sensitive to selection function effects. This proposal aims to: 1) synergise the vast amount of spectro-astro-photometric Milky Way data from the Gaia satellite mission and large-scale spectroscopic surveys; 2) build on the Orbital Torus Imaging (OTI) framework that uses element abundance gradients in phase space, and construct a data-driven generative model to measure the spatial, orbital, and mass distribution in the Milky Way; 3) use OTI to measure the amount of dark matter across the Milky Way; 4) use OTI to measure the amount of disequilibrium in the Milky Way. The results from this research programme will place constraints on current galaxy formation models.
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