A Data-driven Event Generator for Hadron Colliders using Wasserstein Generative Adversarial Network

TL;DR

This paper introduces a Wasserstein GAN-based method for fast and accurate event generation in high energy physics, reducing computational costs while maintaining high fidelity to real data.

Contribution

The paper presents a novel WGAN approach for simulating particle collision events, offering significant speed improvements over traditional Monte Carlo generators.

Findings

High fidelity reproduction of real data distributions

Significantly faster event generation process

Potential to reduce computational resource requirements

Abstract

Highly reliable Monte-Carlo event generators and detector simulation programs are important for the precision measurement in the high energy physics. Huge amounts of computing resources are required to produce a sufficient number of simulated events. Moreover, simulation parameters have to be fine-tuned to reproduce situations in the high energy particle interactions which is not trivial in some phase spaces in physics interests. In this paper, we suggest a new method based on the Wasserstein Generative Adversarial Network (WGAN) that can learn the probability distribution of the real data. Our method is capable of event generation at a very short computing time compared to the traditional MC generators. The trained WGAN is able to reproduce the shape of the real data with high fidelity.