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

## TL;DR

This paper introduces DijetGAN, a convolutional neural network-based generative adversarial network designed to simulate QCD dijet events at the LHC, achieving high fidelity in reproducing key kinematic distributions at both truth and detector levels.

## Contribution

The paper presents a novel GAN architecture tailored for simulating LHC dijet events, demonstrating accurate reproduction of complex kinematic distributions, with code available publicly.

## Key findings

- High agreement between generated and real kinematic distributions
- Effective simulation of detector effects using GAN
- Open-source code for community use

## Abstract

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 with a very good level of agreement. The code can be checked out or forked from the publicly accessible online repository https://gitlab.cern.ch/disipio/DiJetGAN .