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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 registered SciPost users): | Tilman Plehn · Armand Rousselot · Ramon Winterhalder |
| Submission information | |
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| Preprint Link: | https://arxiv.org/abs/2006.06685v3 (pdf) |
| Date accepted: | Nov. 10, 2020 |
| Date submitted: | Oct. 2, 2020, 10:44 a.m. |
| Submitted by: | Ramon Winterhalder |
| Submitted to: | SciPost Physics |
| Ontological classification | |
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| 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)