Dark matter, constituting a fifth of the mass-energy in the Universe today, is one of the major "known unknowns" in physics. A number of different experimental and observational techniques exist to try to identify dark matter. However, these techniques are not only sensitive to the "physics" of dark matter (mass, cross sections, and the theory in which the dark matter particles live) but to the "astrophysics" of dark matter as well, namely the phase-space density of dark matter throughout the Milky Way and other galaxies and its evolution through cosmic time.
In order to accurately map signals in experiments or observations to the particle-physics properties of dark matter, we need to understand the astrophysics of dark matter. In this talk, I will demonstrate how to get robust constraints on the particle-physics properties of dark matter either by careful modeling the astrophysics properties of dark matter or by elevating the astrophysics properties of dark matter as something to be extracted from future data sets alongside particle-physics parameters, and which approach (modeling vs. empirical) is more useful for given problems.
As an example, I will show which aspects of the local dark-matter phase-space density can be understood through modeling and which aspects may be possible to infer empirically, and what the implications are for determining the particle-physics of dark matter from direct and indirect detection.