Statistical challenges on an astronomical scale: Bayesian estimates of the Milky Way’s dark matter halo
Evidence suggests that every galaxy in the universe resides in a giant cloud of dark matter, called a “dark matter halo”. These dark matter halos can make up more than 80% of a galaxy’s mass, and are expected to play an important role in a galaxy’s fate. However, obtaining precise measurements of a galaxy’s dark matter halo is difficult; challenges include incomplete data, measurement uncertainty, model bias, and questions of identifiability. Moreover, we cannot detect dark matter directly. Current theory postulates that dark matter is an unknown subatomic particle that interacts with regular matter through gravity alone. Thus, we must infer the dark matter halo’s presence and mass through its effects on regular matter. In this talk, I will discuss the hierarchical Bayesian model we have developed to estimate the total mass and cumulative mass profile of the Milky Way’s dark matter halo, and how our method helps to overcome some of the statistical challenges associated with dark matter halo measurements.