We present an implementation of an explainable and physics-aware machine
learning model capable of inferring the underlying physics of high-energy
particle collisions using the information encoded in the energy-momentum
four-vectors of the final state particles. We demonstrate the proof-of-concept
of our White Box AI approach using a Generative Adversarial Network (GAN) which
learns from a DGLAP-based parton shower Monte Carlo event generator. We show,
for the first time, that our approach leads to a network that is able to learn
not only the final distribution of particles, but also the underlying parton
branching mechanism, i.e. the Altarelli-Parisi splitting function, the ordering
variable of the shower, and the scaling behavior. While the current work is
focused on perturbative physics of the parton shower, we foresee a broad range
of applications of our framework to areas that are currently difficult to
address from first principles in QCD. Examples include nonperturbative and
collective effects, factorization breaking and the modification of the parton
shower in heavy-ion, and electron-nucleus collisions.

我们提出了一种可解释的、物理感知的机器学习模型的实现，能够推断高能粒子碰撞的基础物理，利用最终状态粒子的能量 - 动量四矢量中的信息。我们使用生成对抗网络（GAN）的概念证明了我们的白盒人工智能方法，该方法学习自 DGLAP 型发射子阶级蒙特卡洛事例发生器。我们首次展示了我们的方法不仅能学习粒子的最终分布，还能学习阶级分支机制 —— 即 Altarelli-Parisi 分裂函数、阶级的序列变量和缩放行为。虽然当前工作集中在发射子阶级的扰动物理学上，但我们预见到我们的框架在目前难以从 QCD 的第一原则解决的领域中具有广泛的应用，如非微扰和集体效应、分解破坏和重离子和电子 - 核碰撞中的发射子阶级修改。