论文标题
使用变压器编码器并标准化流量的点云生成
Point Cloud Generation using Transformer Encoders and Normalising Flows
论文作者
论文摘要
基于机器学习的数据生成已成为粒子物理学的主要研究主题。这是由于当前的蒙特卡洛模拟方法对未来的围墙具有计算性挑战，这将具有明显更高的发光度。对撞机数据的产生类似于点云的生成，但可以说要困难得多，因为两点之间存在复杂的相关性，需要正确建模。提出了一个由标准化流和变压器编码器组成的改进模型。归一化流量输出通过变压器编码器纠正，该编码器是针对另一个变压器编码器歧视器/评论家对抗的。该模型达到了最先进的性能，同时进行了稳定的培训。
Data generation based on Machine Learning has become a major research topic in particle physics. This is due to the current Monte Carlo simulation approach being computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation, but arguably more difficult as there are complex correlations between the points which need to be modelled correctly. A refinement model consisting of normalising flows and transformer encoders is presented. The normalising flow output is corrected by a transformer encoder, which is adversarially trained against another transformer encoder discriminator/critic. The model reaches state-of-the-art performance while yielding a stable training.
