CaloFlow: Fast and Accurate Generation of Calorimeter Showers with Normalizing Flows
This provides a faster alternative to computationally expensive GEANT4 simulations for particle physics researchers, though it is incremental as it builds on existing normalizing flow methods.
The paper tackled the problem of simulating calorimeter showers in particle physics by introducing CaloFlow, a fast detector simulation framework based on normalizing flows, which achieved high fidelity in reproducing many-channel showers and outperformed GANs in fooling a classifier with nearly 100% accuracy for GAN-generated images.
We introduce CaloFlow, a fast detector simulation framework based on normalizing flows. For the first time, we demonstrate that normalizing flows can reproduce many-channel calorimeter showers 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 nearly 100% accuracy, while images generated from CaloFlow are better able to fool the classifier. 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.