تقرير
Particle Cloud Generation with Message Passing Generative Adversarial Networks
| العنوان: | Particle Cloud Generation with Message Passing Generative Adversarial Networks |
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| المؤلفون: | Kansal, Raghav, Duarte, Javier, Su, Hao, Orzari, Breno, Tomei, Thiago, Pierini, Maurizio, Touranakou, Mary, Vlimant, Jean-Roch, Gunopulos, Dimitrios |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science High Energy Physics - Experiment |
| مصطلحات موضوعية: | Computer Science - Machine Learning, High Energy Physics - Experiment |
| الوصف: | In high energy physics (HEP), jets are collections of correlated particles produced ubiquitously in particle collisions such as those at the CERN Large Hadron Collider (LHC). Machine learning (ML)-based generative models, such as generative adversarial networks (GANs), have the potential to significantly accelerate LHC jet simulations. However, despite jets having a natural representation as a set of particles in momentum-space, a.k.a. a particle cloud, there exist no generative models applied to such a dataset. In this work, we introduce a new particle cloud dataset (JetNet), and apply to it existing point cloud GANs. Results are evaluated using (1) 1-Wasserstein distances between high- and low-level feature distributions, (2) a newly developed Fr\'{e}chet ParticleNet Distance, and (3) the coverage and (4) minimum matching distance metrics. Existing GANs are found to be inadequate for physics applications, hence we develop a new message passing GAN (MPGAN), which outperforms existing point cloud GANs on virtually every metric and shows promise for use in HEP. We propose JetNet as a novel point-cloud-style dataset for the ML community to experiment with, and set MPGAN as a benchmark to improve upon for future generative models. Additionally, to facilitate research and improve accessibility and reproducibility in this area, we release the open-source JetNet Python package with interfaces for particle cloud datasets, implementations for evaluation and loss metrics, and more tools for ML in HEP development. Comment: 14 pages, 4 figures, 2 tables, and an 8 page appendix. Accepted to the Thirty-fifth Conference on Neural Information Processing Systems |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2106.11535 |
| رقم الأكسشن: | edsarx.2106.11535 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |