Event Generation and Density Estimation with Surjective Normalizing Flows

Understand

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.