Juniorprofessor Dr. Felix Kahlhöfer

Solving the puzzle of dark matter requires not only a broad experimental effort, but also a theoretical framework capable of accurately predicting dark matter signals in many different experiments and consistently interpreting the available data in terms of the underlying model parameters. In my talk, I will introduce the global fitting framework GAMBIT, which enables the fast and flexible exploration of almost arbitrary models of physics beyond the Standard Model. As a specific application, I will discuss the impact of the most recent LHC dark matter searches and direct detection experiments on effective theories and simplified models of dark matter. Furthermore, I will present recently developed tools and machine-learning techniques, which make it possible to combine information from laboratory experiments with astrophysical and cosmological observations, and illustrate the implications for scalar singlet dark matter and axion-like particles.