A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network

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-t...

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Veröffentlicht in:Journal of the Korean Physical Society 2021, 78(6), , pp.482-489
Hauptverfasser: Choi, Suyong, Lim, Jae Hoon
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:0374-4884
1976-8524
DOI:10.1007/s40042-021-00095-1