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A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC
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 MadGraph5, Pythia8, and Delphes3 fast detector simulation. We demonstrate that a number of kinematic distribution...
| Autores principales: | , , , |
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| Lenguaje: | eng |
| Publicado: |
SISSA
2019
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.22323/1.367.0050 http://cds.cern.ch/record/2728092 |
| _version_ | 1780966448321003520 |
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| author | Di Sipio, Riccardo Faucci Giannelli, Michele Ketabchi Haghighat, Sana Palazzo, Serena |
| author_facet | Di Sipio, Riccardo Faucci Giannelli, Michele Ketabchi Haghighat, Sana Palazzo, Serena |
| author_sort | Di Sipio, Riccardo |
| collection | CERN |
| description | 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 MadGraph5, Pythia8, and Delphes3 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. |
| id | oai-inspirehep.net-1774967 |
| institution | Organización Europea para la Investigación Nuclear |
| language | eng |
| publishDate | 2019 |
| publisher | SISSA |
| record_format | invenio |
| spelling | oai-inspirehep.net-17749672020-12-14T13:46:11Zdoi:10.22323/1.367.0050http://cds.cern.ch/record/2728092engDi Sipio, RiccardoFaucci Giannelli, MicheleKetabchi Haghighat, SanaPalazzo, SerenaA Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHCParticle Physics - ExperimentA 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 MadGraph5, Pythia8, and Delphes3 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.SISSAoai:inspirehep.net:17749672019 |
| spellingShingle | Particle Physics - Experiment Di Sipio, Riccardo Faucci Giannelli, Michele Ketabchi Haghighat, Sana Palazzo, Serena A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC |
| title | A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC |
| title_full | A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC |
| title_fullStr | A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC |
| title_full_unstemmed | A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC |
| title_short | A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC |
| title_sort | generative-adversarial network approach for the simulation of qcd dijet events at the lhc |
| topic | Particle Physics - Experiment |
| url | https://dx.doi.org/10.22323/1.367.0050 http://cds.cern.ch/record/2728092 |
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