Self-consistent annihilation simulations of dark matter
A student will work on Self-consistent annihilation simulations of dark matter and develop self-consistent self-annihilation N-body simulations, in which dark matter mass is removed on-the-fly in simulations as interactions take place. This requires the development of an efficient, massively-parallel code which can do neighbour searches during simulations and implement annihilation in a probabilistic manner. The specific questions are: what are the effects of annihilation history of galaxies on predicted annihilation signal at the present day; what is the predicted spatial distribution of the signal and can it be used to distinguish a dark matter origin from astrophysical sources of contamination; what are the forecasts for the CTA, in terms of being able to constrain the nature of dark matter. This will pull on advanced machine learning techniques that have been developed by LBDN researchers to efficiently calculate N-body interactions. A placement at Brainbox will cover data acquisition and analysis techniques.