论文标题
高能量物理中稀疏数据生成的图形生成对抗网络
Graph Generative Adversarial Networks for Sparse Data Generation in High Energy Physics
论文作者
论文摘要
我们开发了一个图形生成对抗网络，以生成稀疏的数据集，例如在CERN大型强子对撞机（LHC）上产生的数据集。我们通过训练和生成质子蛋白质碰撞中的MNIST手写数字图像和颗粒喷射的稀疏表示来证明这种方法，例如LHC。我们发现该模型成功生成了稀疏的MNIST数字和粒子喷气数据。我们量化了具有基于图的Fréchet成立距离的真实数据和生成的数据之间的一致性，以及分别为MNIST和JET数据集的粒子和JET特征级别的1-WassErstein距离。
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 Fréchet Inception distance, and the particle and jet feature-level 1-Wasserstein distance for the MNIST and jet datasets respectively.
