تقرير
LHC analysis-specific datasets with Generative Adversarial Networks
| العنوان: | LHC analysis-specific datasets with Generative Adversarial Networks |
|---|---|
| المؤلفون: | Hashemi, Bobak, Amin, Nick, Datta, Kaustuv, Olivito, Dominick, Pierini, Maurizio |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science High Energy Physics - Experiment High Energy Physics - Phenomenology |
| مصطلحات موضوعية: | High Energy Physics - Experiment, Computer Science - Machine Learning, High Energy Physics - Phenomenology |
| الوصف: | Using generative adversarial networks (GANs), we investigate the possibility of creating large amounts of analysis-specific simulated LHC events at limited computing cost. This kind of generative model is analysis specific in the sense that it directly generates the high-level features used in the last stage of a given physics analyses, learning the N-dimensional distribution of relevant features in the context of a specific analysis selection. We apply this idea to the generation of muon four-momenta in $Z \to \mu\mu$ events at the LHC. We highlight how use-case specific issues emerge when the distributions of the considered quantities exhibit particular features. We show how substantial performance improvements and convergence speed-up can be obtained by including regression terms in the loss function of the generator. We develop an objective criterion to assess the geenrator performance in a quantitative way. With further development, a generalization of this approach could substantially reduce the needed amount of centrally produced fully simulated events in large particle physics experiments. Comment: 14 pages, 11 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1901.05282 |
| رقم الأكسشن: | edsarx.1901.05282 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |