15-18 April 2019
CERN
Europe/Zurich timezone
There is a live webcast for this event.

DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC

17 Apr 2019, 09:35
20m
500/1-001 - Main Auditorium (CERN)

500/1-001 - Main Auditorium

CERN

400
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Speaker

Serena Palazzo (The University of Edinburgh (GB))

Description

In this talk, I will present a Generative-Adversarial Network (GAN) based on convolutional neural networks that 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. A number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network with a very good level of agreement.

Preferred contribution length 20 minutes

Primary authors

Michele Faucci Giannelli (University of Edinburgh) Riccardo Di Sipio (University of Toronto (CA)) Sana Ketabchi (University of Toronto (CA)) Serena Palazzo (The University of Edinburgh (GB))

Presentation Materials