Induced Generative Adversarial Particle Transformers
Anni Li Venkat Krishnamohan Raghav Kansal Rounak Sen Steven Tsan Zhaoyu Zhang Javier Duarte
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.