تقرير
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
| العنوان: | Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics |
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| المؤلفون: | Kansal, Raghav, Duarte, Javier, Orzari, Breno, Tomei, Thiago, Pierini, Maurizio, Touranakou, Mary, Vlimant, Jean-Roch, Gunopulos, Dimitrios |
| سنة النشر: | 2020 |
| المجموعة: | Computer Science High Energy Physics - Experiment High Energy Physics - Phenomenology Physics (Other) |
| مصطلحات موضوعية: | Physics - Data Analysis, Statistics and Probability, Computer Science - Machine Learning, High Energy Physics - Experiment, High Energy Physics - Phenomenology, Physics - Computational Physics |
| الوصف: | We develop a graph generative adversarial network to generate sparse data sets like those produced at the CERN Large Hadron Collider (LHC). We demonstrate this approach by training on and generating sparse representations of MNIST handwritten digit images and jets of particles in proton-proton collisions like those at the LHC. We find the model successfully generates sparse MNIST digits and particle jet data. We quantify agreement between real and generated data with a graph-based Fr\'echet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively. Comment: 9 pages, 4 figures, 4 tables, To appear in Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2012.00173 |
| رقم الأكسشن: | edsarx.2012.00173 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |