论文标题
使用协变量粒子变压器预测顶级夸克运动特性的整体方法
A Holistic Approach to Predicting Top Quark Kinematic Properties with the Covariant Particle Transformer
论文作者
论文摘要
由于组合背景和缺少信息，在大型强子对撞机上，顶级夸克属性的精确重建是一项具有挑战性的任务。我们引入了一个称为协变粒子变压器（CPT）的物理信息的神经网络体系结构，用于直接预测重建的最终状态对象的顶级夸克运动特性。这种方法是置换不变的，部分是洛伦兹协变量，可以解释可变数量的输入对象。与以前的基于机器学习的重建方法相反，CPT能够预测顶级Quark四摩Momenta，而不管事件发生什么Jet多样性。使用模拟，我们表明CPT与其他机器学习顶级夸克重建方法相比表现出色。
Precise reconstruction of top quark properties is a challenging task at the Large Hadron Collider due to combinatorial backgrounds and missing information. We introduce a physics-informed neural network architecture called the Covariant Particle Transformer (CPT) for directly predicting the top quark kinematic properties from reconstructed final state objects. This approach is permutation invariant and partially Lorentz covariant and can account for a variable number of input objects. In contrast to previous machine learning-based reconstruction methods, CPT is able to predict top quark four-momenta regardless of the jet multiplicity in the event. Using simulations, we show that the CPT performs favorably compared with other machine learning top quark reconstruction approaches.
