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.

该研究介绍了一种新颖的方法，利用最新的机器学习技术从部分子水平信息直接生成探测器水平的完整事件。通过使用变压器、基于评分的模型和归一化流，解决了部分子空间和重建对象空间之间多样性变化的挑战，克服了这两个空间之间的随机转移的固有复杂性，并实现了显著准确的结果。创新技术的结合以及所实现的准确性展示了我们方法在推动该领域发展方面的潜力，并为进一步探索开辟了道路。该研究对高能物理学和生成建模的持续努力做出了贡献，为快速探测器模拟提供了有希望的方向。