论文标题
事件产生和密度估计，汇总流量正常
Event Generation and Density Estimation with Surjective Normalizing Flows
论文作者
论文摘要
标准化流量是一类生成模型，可实现精确的可能性评估。尽管这些模型已经在粒子物理学中发现了各种应用，但标准化流的灵活性不足以建模碰撞事件的许多外围特征。使用Nielsen等人的框架。 （2020），我们将几个过滤和随机变换层引入基线归一化流，以改善置换对称性的建模，不同的维度和离散特征，这些特征都在粒子物理事件中通常遇到。我们在生成矩阵元素级过程的背景下以及在检测级LHC事件中的异常检测中评估它们的功效。
Normalizing flows are a class of generative models that enable exact likelihood evaluation. While these models have already found various applications in particle physics, normalizing flows are not flexible enough to model many of the peripheral features of collision events. Using the framework of Nielsen et al. (2020), we introduce several surjective and stochastic transform layers to a baseline normalizing flow to improve modelling of permutation symmetry, varying dimensionality and discrete features, which are all commonly encountered in particle physics events. We assess their efficacy in the context of the generation of a matrix element-level process, and in the context of anomaly detection in detector-level LHC events.
