Constraining the nature of dark matter with Galactic halo substructures

Durham University
Physics

One of the major unsolved problems in physics is the nature of dark matter. This “missing mass” of the Universe is thought to account for 85% of all matter and is responsible for binding the stars in galaxies together and the location of galaxies on much larger scales. Its exact nature has fundamental implications for physics from cosmology to particle physics. However, it does not emit light for our telescopes to capture, and our best efforts to detect it using particle physics experiments have not yet succeeded. One of the most promising avenues to study dark matter is through its gravitational pull on stars and galaxies, particularly those that surround our Milky Way (MW) galaxy for which we have detailed and precise measurements.

In recent years, the Gaia satellite has revolutionized our understanding of the MW by precisely measuring the movements of more than a billion stars. We now have positions and velocities for every “satellite” galaxy that orbits around the MW. These observations seem to be in tension with theoretical predictions from genericcosmological simulations: the satellites move coherently, and are preferentially located close to the MW. This project will be the first to instead use simulations tailored to the MW’s growth history while accounting for observational biases from our telescopes. By using these more sophisticated comparisons between simulations and observations, I will determine if alternative models of dark matter are needed to explain these strange orbital properties.

Secondly, our current models predict clumps of dark matter orbiting the MW that contain no stars. The most promising way to detect these clumps is through their gravitational interactions with star clusters that are disrupting due to the MW’s tidal forces to form long, thin streams of stars stretched along the sky. I will use an innovative approach combining tailored cosmological simulations of the MW halo with numerical modeling to generate streams. I will make predictions for the full population of streams that future telescopes will detect and evaluate the prospect of discovering low-mass dark matter substructures (devoid of stars), which will place constraints on the properties of dark matter.