Heidelberg 2022 – wissenschaftliches Programm
T 86.6: Vortrag
Donnerstag, 24. März 2022, 17:30–17:45, T-H17
Constraining dark matter annihilation with cosmic ray antiprotons using neural networks — Felix Kahlhoefer1, Michael Korsmeier2, Michael Krämer1, Silvia Manconi1, and •Kathrin Nippel1 — 1Institute for Theoretical Particle Physics and Cosmology (TTK), RWTH Aachen University, D-52056 Aachen, Germany — 2The Oskar Klein Centre for Cosmoparticle Physics, Department of Physics, Stockholm University, Alba Nova, 10691 Stockholm, Sweden
The interpretation of data from indirect detection experiments searching for dark matter annihilations requires computationally expensive simulations of cosmic-ray propagation. We present new methods based on Recurrent and Bayesian Neural Networks that significantly accelerate simulations of secondary and dark matter Galactic cosmic ray antiprotons by at least two orders of magnitude compared to conventional approaches while achieving excellent accuracy. This approach allows for an efficient profiling or marginalisation over the nuisance parameters of a cosmic ray propagation model in order to perform parameter scans for a wide range of dark matter models. We present resulting constraints using the most recent AMS-02 antiproton data on several models of Weakly Interacting Massive Particles.