# Invertible Networks or Partons to Detector and Back Again

Source: [Invertible Networks or Partons to Detector and Back Again](<https://www.sota2.com/research/paper/68fa3d881e209131ce8e10c3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T01:33:57.965Z

Authors: Marco Bellagente, Anja Butter, Gregor Kasieczka, Tilman Plehn, Armand Rousselot, Ramon Winterhalder, Lynton Ardizzone, Ullrich K\\"othe

Published: 2020-06-11T18:00:00.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Kinematic Unfolding on Simulated semi-leptonic tt production v2.7.2 (MADGRAPH\_AMC@NLO) (test)](<https://www.sota2.com/research/sota/kinematic-unfolding-on-simulated-semi-leptonic-tt-production-v2-7-2-madgraph_amc-at-nlo-test.md>): Kinematic Unfolding; Simulated semi-leptonic tt production v2.7.2 (MADGRAPH\_AMC@NLO) (test); Wasserstein Distance: 3009.08.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2006.06685>)

[PDF](<https://arxiv.org/pdf/2006.06685.pdf>)