Generative networks for LHC events
LHC physics crucially relies on our ability to simulate events efficiently from first principles. Modern machine learning, specifically generative networks, will help us tackle simulation challenges for the coming LHC runs. Such networks can be employed within established simulation tools or as part...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) Kapitel/Artikel |
| Sprache: | Englisch |
| Veröffentlicht: |
19 Aug 2020
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| In: |
Arxiv
Year: 2020, Pages: 1-41 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2008.08558
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| Verfasserangaben: | Anja Butter and Tilman Plehn |
| Zusammenfassung: | LHC physics crucially relies on our ability to simulate events efficiently from first principles. Modern machine learning, specifically generative networks, will help us tackle simulation challenges for the coming LHC runs. Such networks can be employed within established simulation tools or as part of a new framework. Since neural networks can be inverted, they also open new avenues in LHC analyses. |
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| Beschreibung: | Gesehen am 09.11.2022 |
| Beschreibung: | Online Resource |