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.
@article{arxiv.2406.13074,
title = {PIPPIN: Generating variable length full events from partons},
author = {Guillaume Quétant and John Andrew Raine and Matthew Leigh and Debajyoti Sengupta and Tobias Golling},
journal= {arXiv preprint arXiv:2406.13074},
year = {2024}
}