Lund jet images from generative and cycle-consistent adversarial networks
This provides a framework for reducing simulation times and augmenting data in particle physics, though it appears incremental as it builds on existing generative techniques.
The paper tackles the problem of simulating radiation patterns within jets using the Lund jet plane by introducing a generative model that retrieves the underlying two-dimensional distribution to within a few percent, and demonstrates a method to map between different jet categories for retroactive simulation adjustments.
We introduce a generative model to simulate radiation patterns within a jet using the Lund jet plane. We show that using an appropriate neural network architecture with a stochastic generation of images, it is possible to construct a generative model which retrieves the underlying two-dimensional distribution to within a few percent. We compare our model with several alternative state-of-the-art generative techniques. Finally, we show how a mapping can be created between different categories of jets, and use this method to retroactively change simulation settings or the underlying process on an existing sample. These results provide a framework for significantly reducing simulation times through fast inference of the neural network as well as for data augmentation of physical measurements.