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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
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Submission summary
| Authors (as Contributors): | Tilman Plehn · Armand Rousselot · Ramon Winterhalder |
| Submission information | |
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| 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 |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| 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)
Submission & Refereeing History
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Reports on this Submission
Anonymous Report 3 on 2020-11-3 (Invited Report)
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In the revised version of the manuscript the authors have addressed comments of all the reviewers. I recommend the paper for publication.
Anonymous Report 2 on 2020-11-3 (Invited Report)
Report
The authors have satisfactorily addressed my comments and I recommend the manuscript for publication.
Anonymous Report 1 on 2020-10-20 (Invited Report)
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The authors have addressed my main comments and I recommend the article for publication in SciPost