Simulation-Prior Independent Neural Unfolding Procedure

Anja Butter, Theo Heimel, Nathan Huetsch, Michael Kagan, Tilman Plehn
7/20/2025
hep-phcs.LGhep-ex

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Abstract

Machine learning allows unfolding high-dimensional spaces without binning at the LHC. The new SPINUP method extracts the unfolded distribution based on a neural network encoding the forward mapping, making it independent of the prior from the simulated training data. It is made efficient through neural importance sampling, and ensembling can be used to estimate the effect of information loss in the forward process. We showcase SPINUP for unfolding detector effects on jet substructure observables and for unfolding to parton level of associated Higgs and single-top production.

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Cite this paper

@article{butter2025simulationprior,
  title  = {Simulation-Prior Independent Neural Unfolding Procedure},
  author = {Anja Butter and Theo Heimel and Nathan Huetsch and Michael Kagan and Tilman Plehn},
  year   = {2025},
  eprint = {2507.15084},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2507.15084v1}
}

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