论文标题
建立强扎的深度学习模型
Towards a Deep Learning Model for Hadronization
论文作者
论文摘要
强环化是一个复杂的量子过程，从而使夸克和胶子变成了哈德子。事件发生器中广泛使用的强合化模型基于具有许多自由参数的物理启发的现象学模型。我们提出了一种使用神经网络的替代方法。深生成模型具有高度灵活的，可区分的，并且与图形处理单元（GPU）兼容。我们通过用生成对抗性网络（GAN）在Herwig事件生成器（群集模型）中替换基于数据驱动的机器学习基于数据驱动的机器学习模型的第一步。我们表明，GAN能够再现簇衰变的运动学特性。此外，我们将此模型集成到Herwig中，以生成可以与公共Herwig Simulator的输出以及$ e^+e^ - $数据进行比较的整个事件。
Hadronization is a complex quantum process whereby quarks and gluons become hadrons. The widely-used models of hadronization in event generators are based on physically-inspired phenomenological models with many free parameters. We propose an alternative approach whereby neural networks are used instead. Deep generative models are highly flexible, differentiable, and compatible with Graphical Processing Unit (GPUs). We make the first step towards a data-driven machine learning-based hadronization model by replacing a compont of the hadronization model within the Herwig event generator (cluster model) with a Generative Adversarial Network (GAN). We show that a GAN is capable of reproducing the kinematic properties of cluster decays. Furthermore, we integrate this model into Herwig to generate entire events that can be compared with the output of the public Herwig simulator as well as with $e^+e^-$ data.
