JetFlow：用有条件和质量约束的规范化流生成喷流
JetFlow: Generating Jets with Conditioned and Mass Constrained Normalising Flows
摘要：
基于机器学习的数据快速生成技术已成为粒子物理学的一个重要研究课题。这主要是因为蒙特卡罗模拟方法对于未来的对撞机来说在计算上是具有挑战性的，因为未来的对撞机将具有明显更高的光度。碰撞器数据的生成类似于点云生成，点之间具有复杂的相关性。在这项研究中，生成的射流与多达30个成分的归一化流使用有理二次样条耦合层进行了研究。在不对射流质量进行调节的情况下，我们的归一化流无法正确地对数据中的所有相关性进行建模，这在比较地面真实数据和生成数据之间的不变射流质量分布时是显而易见的。使用不变质量作为耦合变换的条件增强了所有跟踪度量的性能。此外，我们还演示了如何通过插值经验累积分布函数来对原始质量分布进行采样。类似地，通过引入关于射流中组分数目的附加条件来处理组分的可变数目。此外，我们研究的有用性，包括一个额外的质量约束的损失项。在\texttt{JetNet}数据集上，我们的模型显示出最先进的性能，并结合了快速和稳定的训练。
英文摘要：
Fast data generation based on Machine Learning has become a major research topic in particle physics. This is mainly because the Monte Carlo simulation approach is computationally challenging for future colliders, which will have a significantly higher luminosity. The generation of collider data is similar to point cloud generation with complex correlations between the points. In this study, the generation of jets with up to 30 constituents with Normalising Flows using Rational Quadratic Spline coupling layers is investigated. Without conditioning on the jet mass, our Normalising Flows are unable to model all correlations in data correctly, which is evident when comparing the invariant jet mass distributions between ground truth and generated data. Using the invariant mass as a condition for the coupling transformation enhances the performance on all tracked metrics. In addition, we demonstrate how to sample the original mass distribution by interpolating the empirical cumulative distribution function. Similarly, the variable number of constituents is taken care of by introducing an additional condition on the number of constituents in the jet. Furthermore, we study the usefulness of including an additional mass constraint in the loss term. On the \texttt{JetNet} dataset, our model shows state-of-the-art performance combined with fast and stable training.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
