تقرير
A Data-driven Event Generator for Hadron Colliders using Wasserstein Generative Adversarial Network
| العنوان: | A Data-driven Event Generator for Hadron Colliders using Wasserstein Generative Adversarial Network |
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| المؤلفون: | Choi, Suyong, Lim, Jae Hoon |
| سنة النشر: | 2021 |
| المجموعة: | High Energy Physics - Experiment |
| مصطلحات موضوعية: | High Energy Physics - Experiment |
| الوصف: | 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. Comment: To appear in Journal of the Korean Physical Society |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/s40042-021-00095-1 |
| URL الوصول: | http://arxiv.org/abs/2102.11524 |
| رقم الأكسشن: | edsarx.2102.11524 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/s40042-021-00095-1 |
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