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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This work is supported by the National Research Foundation of Korea (NRF) under Contract No. NRF-2018R1A2B6005043, NRF-2020R1A2C3009918, and the BK21 FOUR program at Korea University, Initiative for science frontiers on upcoming challenges.
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Choi, S., Lim, J.H. A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network. J. Korean Phys. Soc. 78, 482–489 (2021). https://doi.org/10.1007/s40042-021-00095-1
- HEP data
- Event generation
- Deep learning