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| Autori principali: | , , , |
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| Natura: | Recurso digital |
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Zenodo
2019
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| Accesso online: | https://doi.org/10.5281/zenodo.14983913 |
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| _version_ | 1866901119924436992 |
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| author | Di Sipio, Riccardo Faucci Giannelli, Michele Ketabchi, Sana Palazzo, Serena |
| author_facet | Di Sipio, Riccardo Faucci Giannelli, Michele Ketabchi, Sana Palazzo, Serena |
| contents | <p>A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using M<span>AD</span>G<span>RAPH</span>5, P<span>YTHIA</span>8, and D<span>ELPHES</span>3 fast detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_14983913 |
| institution | Zenodo |
| language | |
| publishDate | 2019 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DiJetGAN tuples Di Sipio, Riccardo Faucci Giannelli, Michele Ketabchi, Sana Palazzo, Serena <p>A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC. The GAN is trained on events generated using M<span>AD</span>G<span>RAPH</span>5, P<span>YTHIA</span>8, and D<span>ELPHES</span>3 fast detector simulation. We demonstrate that a number of kinematic distributions both at Monte Carlo truth level and after the detector simulation can be reproduced by the generator network.</p> |
| title | DiJetGAN tuples |
| url | https://doi.org/10.5281/zenodo.14983913 |