Lund jet images from generative and cycle-consistent adversarial networks
| Autor: | Carrazza, Stefano, Dreyer, Fr��d��ric A. |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
High Energy Physics - Experiment (hep-ex) High Energy Physics - Phenomenology (hep-ph) Image and Video Processing (eess.IV) FOS: Electrical engineering electronic engineering information engineering FOS: Physical sciences Machine Learning (stat.ML) Machine Learning (cs.LG) |
| DOI: | 10.48550/arxiv.1909.01359 |
| Popis: | 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. 11 pages, 15 figures, code available at https://github.com/JetsGame/gLund and https://github.com/JetsGame/CycleJet, updated to match published version |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|