Speaker
Florian Ernst
Description
Normalizing-flow architectures have shown outstanding performance in various generative tasks at the LHC. However, they don't scale well to higher dimensional datasets. We investigate several directions to improve normalizing flows for calorimeter shower simulations: 1) using a coupling-layer based flow to improve training and generation times without dimensionality reduction, and 2), using a VAE to compress the very high-dimensional datasets 2 and 3 of the CaloChallenge.
Primary authors
Dr
Claudius Krause
(Rutgers University)
David Shih
Florian Ernst
Luigi Favaro
Tilman Plehn
Tilman Plehn
