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Invertible Networks or Partons to Detector and Back Again

by Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, Ullrich Köthe

This Submission thread is now published as SciPost Phys. 9, 074 (2020)

Submission summary

As Contributors: Tilman Plehn · Ramon Winterhalder
Arxiv Link: https://arxiv.org/abs/2006.06685v3 (pdf)
Date accepted: 2020-11-10
Date submitted: 2020-10-02 10:44
Submitted by: Winterhalder, Ramon
Submitted to: SciPost Physics
Academic field: Physics
Specialties:
  • High-Energy Physics - Phenomenology
Approach: Computational

Abstract

For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.

Published as SciPost Phys. 9, 074 (2020)




Reports on this Submission

Anonymous Report 3 on 2020-11-3 (Invited Report)

Report

In the revised version of the manuscript the authors have addressed comments of all the reviewers. I recommend the paper for publication.

  • validity: -
  • significance: -
  • originality: -
  • clarity: -
  • formatting: -
  • grammar: -

Anonymous Report 2 on 2020-11-3 (Invited Report)

Report

The authors have satisfactorily addressed my comments and I recommend the manuscript for publication.

  • validity: -
  • significance: -
  • originality: -
  • clarity: -
  • formatting: -
  • grammar: -

Anonymous Report 1 on 2020-10-20 (Invited Report)

Report

The authors have addressed my main comments and I recommend the article for publication in SciPost

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

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