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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

Submission summary

As Contributors: Tilman Plehn · Ramon Winterhalder
Arxiv Link: https://arxiv.org/abs/2006.06685v2 (pdf)
Date submitted: 2020-07-07 02:00
Submitted by: Winterhalder, Ramon
Submitted to: SciPost Physics
Discipline: Physics
Subject area: 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.

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Submission 2006.06685v2 on 7 July 2020

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