Affiliation:
1. University College London
Abstract
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 [1], 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.
Funder
European Research Council
Subject
General Physics and Astronomy
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献