SciPost Submission Page
Event Generation and Density Estimation with Surjective Normalizing Flows
by Rob Verheyen
This Submission thread is now published as
|As Contributors:||Rob Verheyen|
|Arxiv Link:||https://arxiv.org/abs/2205.01697v2 (pdf)|
|Date submitted:||2022-06-17 08:23|
|Submitted by:||Verheyen, Rob|
|Submitted to:||SciPost Physics|
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.
Published as SciPost Phys. 13, 047 (2022)
List of changes
All points raised by the referees have been addressed.
Submission & Refereeing History
You are currently on this page
Reports on this Submission
Anonymous Report 2 on 2022-7-1 (Invited Report)
The author has addressed all the points that I raised to my satisfaction. The explanations in the manuscript improved and are more clear now. I can recommend this paper for publication.
Anonymous Report 1 on 2022-6-23 (Invited Report)
The author has incorporated and answered all my requests satisfactorily. Therefore, I recommend the paper for publication