In high energy physics (HEP), machine learning methods have emerged as an
effective way to accurately simulate particle collisions at the Large Hadron
Collider (LHC). The message-passing generative adversarial network (MPGAN) was
the first model to simulate collisions as point, or ``particle'', clouds, with
state-of-the-art results, but suffered from quadratic time complexity.
Recently, generative adversarial particle transformers (GAPTs) were introduced
to address this drawback; however, results did not surpass MPGAN. We introduce
induced GAPT (iGAPT) which, by integrating ``induced particle-attention
blocks'' and conditioning on global jet attributes, not only offers linear time
complexity but is also able to capture intricate jet substructure, surpassing
MPGAN in many metrics. Our experiments demonstrate the potential of iGAPT to
simulate complex HEP data accurately and efficiently.

高能物理领域中，机器学习方法在准确模拟大型强子对撞机中的粒子碰撞方面发挥了重要作用。我们引入了一种新的模型 iGAPT，它通过整合 “诱导性粒子注意力模块” 并以全局喷注属性为条件，不仅具有线性时间复杂度，还能捕捉复杂的喷注子结构，在许多指标上超过了 MPGAN，从而展示了 iGAPT 在准确而高效地模拟复杂的高能物理数据方面的潜力。