A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, Suyong | - |
dc.contributor.author | Lim, Jae Hoon | - |
dc.date.accessioned | 2022-03-04T14:40:38Z | - |
dc.date.available | 2022-03-04T14:40:38Z | - |
dc.date.created | 2022-02-09 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.issn | 0374-4884 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/137751 | - |
dc.description.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. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN PHYSICAL SOC | - |
dc.title | A data-driven event generator for Hadron Colliders using Wasserstein Generative Adversarial Network | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Suyong | - |
dc.identifier.doi | 10.1007/s40042-021-00095-1 | - |
dc.identifier.scopusid | 2-s2.0-85101678770 | - |
dc.identifier.wosid | 000620433500010 | - |
dc.identifier.bibliographicCitation | JOURNAL OF THE KOREAN PHYSICAL SOCIETY, v.78, no.6, pp.482 - 489 | - |
dc.relation.isPartOf | JOURNAL OF THE KOREAN PHYSICAL SOCIETY | - |
dc.citation.title | JOURNAL OF THE KOREAN PHYSICAL SOCIETY | - |
dc.citation.volume | 78 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 482 | - |
dc.citation.endPage | 489 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002695918 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Physics | - |
dc.relation.journalWebOfScienceCategory | Physics, Multidisciplinary | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Event generation | - |
dc.subject.keywordAuthor | GAN | - |
dc.subject.keywordAuthor | HEP data | - |
dc.subject.keywordAuthor | WGAN | - |
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