A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network
- Authors
- Choi, Suyong; Lim, Jae Hoon
- Issue Date
- 3월-2021
- Publisher
- KOREAN PHYSICAL SOC
- Keywords
- Deep learning; Event generation; GAN; HEP data; WGAN
- Citation
- JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.78, no.6, pp.482 - 489
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- JOURNAL OF THE KOREAN PHYSICAL SOCIETY
- Volume
- 78
- Number
- 6
- Start Page
- 482
- End Page
- 489
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/137751
- DOI
- 10.1007/s40042-021-00095-1
- ISSN
- 0374-4884
- 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.
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