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Event Generation and Density Estimation with Surjective Normalizing Flows

by Rob Verheyen

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Submission summary

Authors (as registered SciPost users): Rob Verheyen
Submission information
Preprint Link:  (pdf)
Code repository:
Date accepted: 2022-07-05
Date submitted: 2022-06-17 08:23
Submitted by: Verheyen, Rob
Submitted to: SciPost Physics
Ontological classification
Academic field: Physics
  • High-Energy Physics - Phenomenology
Approaches: Computational, Phenomenological


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.

List of changes

All points raised by the referees have been addressed.

Published as SciPost Phys. 13, 047 (2022)

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.

  • validity: high
  • significance: top
  • originality: high
  • clarity: high
  • formatting: excellent
  • grammar: excellent

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

  • validity: top
  • significance: high
  • originality: good
  • clarity: high
  • formatting: excellent
  • grammar: excellent

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