Published August 21, 2019 | Version v1
Dataset Open

DiJetGAN tuples

Authors/Creators

  • 1. Ideal O5 Systems
  • 2. ROR icon University of Edinburgh

Description

A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using MADGRAPH5, PYTHIA8, and DELPHES3 fast detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network.

Files

csv.zip

Files (1.5 GB)

Name Size Download all
md5:6abd9eb842a7c039a132b43eb19181f9
578.5 MB Preview Download
md5:8e68a40cc4c486aff4aa48902aa9c2c4
56.9 MB Preview Download
md5:8a11243fd48458835d28258cc450417b
879.7 MB Preview Download

Additional details

Software

Repository URL
https://github.com/rdisipio/DiJetGAN
Programming language
Python
Development Status
Moved

References