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

Understand

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.