Fast and accurate simulations of calorimeter showers with normalizing flows
Abstract
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 generative adversarial networks (GANs) or variational autoencoders (VAEs). In addition to 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.
- Publication:
-
Physical Review D
- Pub Date:
- June 2023
- DOI:
- 10.1103/PhysRevD.107.113003
- arXiv:
- arXiv:2106.05285
- Bibcode:
- 2023PhRvD.107k3003K
- Keywords:
-
- Physics - Instrumentation and Detectors;
- Computer Science - Machine Learning;
- High Energy Physics - Experiment;
- High Energy Physics - Phenomenology;
- Physics - Data Analysis;
- Statistics and Probability
- E-Print:
- 33 pages, 19 figures, 5 tables