PIPPIN: Generating variable length full events from partons
This research advances fast detector simulation for high-energy physics, though it appears incremental as it combines existing machine learning techniques.
The paper tackles the challenge of generating variable-length full detector-level events from parton-level information in high-energy physics, achieving remarkably accurate results using transformers, score-based models, and normalizing flows.
This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To address the challenge of multiplicity variations between parton and reconstructed object spaces, we employ transformers, score-based models and normalizing flows. Our method tackles the inherent complexities of the stochastic transition between these two spaces and achieves remarkably accurate results. The combination of innovative techniques and the achieved accuracy demonstrates the potential of our approach in advancing the field and opens avenues for further exploration. This research contributes to the ongoing efforts in high-energy physics and generative modelling, providing a promising direction for enhanced precision in fast detector simulation.