A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network
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.
- Publication:
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Journal of Korean Physical Society
- Pub Date:
- March 2021
- DOI:
- 10.1007/s40042-021-00095-1
- arXiv:
- arXiv:2102.11524
- Bibcode:
- 2021JKPS...78..482C
- Keywords:
-
- HEP data;
- Event generation;
- Deep learning;
- GAN;
- WGAN;
- High Energy Physics - Experiment
- E-Print:
- To appear in Journal of the Korean Physical Society