Jul 6 – 8, 2021
Europe/Zurich timezone

CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows

Jul 7, 2021, 2:00 PM


Dr Claudius Krause (Rutgers University)


We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce high-granularity calorimeter simulations with extremely high fidelity, providing a fresh alternative to computationally expensive GEANT4 simulations, as well as other state-of-the-art fast simulation frameworks based on GANs and VAEs. Besides the usual histograms of physical features and images of calorimeter showers, we introduce a new metric for judging the quality of generative modeling: the performance of a classifier trained to differentiate real from generated images. We show that GAN-generated images can be identified by the classifier with 100% accuracy, while images generated from CaloFlow are able to fool the classifier much of the time. More broadly, normalizing flows offer several advantages compared to other state-of-the-art approaches (GANs and VAEs), including: tractable likelihoods; stable and convergent training; and principled model selection. Normalizing flows also provide a bijective mapping between data and the latent space, which could have other applications beyond simulation, for example, to detector unfolding.

Academic Rank Postdoc
Affiliation Rutgers University

Primary authors

Dr Claudius Krause (Rutgers University) David Shih (Rutgers University)

Presentation materials