Computation of Dark Matter Signals in Graphene Detectors

Abstract

Modern cosmology proposes the existence of some unknown substance constituting 85% of the mass of the universe. This substance has been named dark matter and has been hypothesised to be composed of an as of yet unknown weakly interacting particle. Recently, the use of graphene as target material for the direct detection of dark matter has been suggested. This entails the study of the dark matter induced ejection of graphene valence electrons. In this thesis, we calculate the rate of graphene valence electron ejection under the assumption of different dark matter models, specifically where the squared modulus of the scattering amplitude scales like |q|2 or |q|4, where q is the momentum transfer. These models have not previously been considered. We initially derive analytic expressions for the electron ejection rate of these models, which we then evaluate numerically through the use of Python. The ejection rate is then plotted as a function of the electron ejection energy. We find that the electron ejection rate declines less rapidly in the models studied here than in the case of a constant scattering amplitude, i.e. the only case previously studied. The main challenge in these calculations is the high complexity of multidimensional integrals, the evaluation of which necessitated approximately 15,000 core hours.