We introduce a novel machine learning method developed for the fast simulation of calorimeter detector response, adapting vector-quantized variational autoencoder (VQ-VAE). Our model adopts a two-stage generation strategy: initially compressing geometry-aware calorimeter data into a discrete latent space, followed by the application of a sequence model to learn and generate the latent tokens. Extensive experimentation on the Calo-challenge dataset underscores the efficiency of our approach, showcasing a remarkable improvement in the generation speed compared with conventional method by a factor of 2000. Remarkably, our model achieves the generation of calorimeter showers within milliseconds. Furthermore, comprehensive quantitative evaluations across various metrics are performed to validate physics performance of generation.

An artificial intelligence tool has examined physical systems and not surprisingly, found new ways of describing what it found. How do we make sense of the universe? At its most basic, physics helps us understand the relationships between "observable" variables – these are things we can measure. Some variables like acceleration can be reduced to more fundamental variables. These are all variables in physics which shape our understanding of the world.