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.

Lund jet images from generative and cycle-consistent adversarial networks / S. Carrazza, F. Dreyer. - In: EPJ WEB OF CONFERENCES. - ISSN 2101-6275. - 79:11(2019 Nov 27). [10.1140/epjc/s10052-019-7501-1]

Lund jet images from generative and cycle-consistent adversarial networks

S. Carrazza;
2019

Abstract

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.
Settore FIS/02 - Fisica Teorica, Modelli e Metodi Matematici
   Proton strucure for discovery at the Large Hadron Collider (NNNPDF)
   NNNPDF
   EUROPEAN COMMISSION
   H2020
   740006
27-nov-2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/695085
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