Speaker
Timo Janßen
(University of Göttingen)
Description
I present a neural network based approach to phase space sampling in high-energy physics. The main idea is to use Normalizing Flows to remap physics-motivated sampling distributions in order to increase the sampling efficiency. The bijectivity of Normalizing Flows thereby guarantees full phase space coverage and an unbiased reproduction of the desired target distribution. Results for representative examples demonstrate the potential of this approach. I reflect on this in the context of recent developments and discuss possibilities for further improvements.
Primary authors
Enrico Bothmann
Max Knobbe
Steffen Schumann
Timo Janßen
(University of Göttingen)
Tobias Schmale
