Boosting (In)direct Detection of Dark Matter
  In this thesis, I study the expected direct and indirect detection signals of
dark matter. More precisely, I study three aspects of dark matter; I use
hydrodynamic simulations to extract properties of weakly interacting dark
matter that are relevant for both direct and indirect detection signals, and
construct viable dark matter models with interesting experimental signatures.
First, I analyze the full scale Illustris simulation, and find that Galactic
indirect detection signals are expected to be largely symmetric, while
extragalactic signals are not, due to recent mergers and the presence of
substructure. Second, through the study of the high resolution Milky Way
simulation Eris, I find that metal-poor halo stars can be used as tracers for
the dark matter velocity distribution. I use the Sloan Digital Sky Survey to
obtain the first empirical velocity distribution of dark matter, which weakens
the expected direct detection limits by up to an order of magnitude at masses
$\lesssim 10$ GeV. Finally, I expand the weakly interacting dark matter
paradigm by proposing a new dark matter model called boosted dark matter. This
novel scenario contains a relativistic component with interesting hybrid direct
and indirect detection signatures at neutrino experiments. I propose two search
strategies for boosted dark matter, at Cherenkov-based experiments and future
liquid-argon neutrino detectors.
