SciPost Submission Page
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
|Authors (as Contributors):||Tilman Plehn · Armand Rousselot · Ramon Winterhalder|
|Arxiv Link:||https://arxiv.org/abs/2006.06685v3 (pdf)|
|Date submitted:||2020-10-02 10:44|
|Submitted by:||Winterhalder, Ramon|
|Submitted to:||SciPost Physics|
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)
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)
The authors have satisfactorily addressed my comments and I recommend the manuscript for publication.
Anonymous Report 1 on 2020-10-20 (Invited Report)
The authors have addressed my main comments and I recommend the article for publication in SciPost