https://doi.org/10.1140/epjc/s10052-019-7501-1
Regular Article - Theoretical Physics
Lund jet images from generative and cycle-consistent adversarial networks
1
TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Milan, Via Celoria 16, 20133, Milan, Italy
2
Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford, OX1 3PU, UK
* e-mail: stefano.carrazza@cern.ch
Received:
8
September
2019
Accepted:
19
November
2019
Published online:
27
November
2019
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.
© The Author(s), 2019
