SciPost Submission Page
Simulation-Prior Independent Neural Unfolding Procedure
by Anja Butter, Theo Heimel, Nathan Huetsch, Michael Kagan, Tilman Plehn
Submission summary
| Authors (as registered SciPost users): | Theo Heimel · Nathan Huetsch · Tilman Plehn |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2507.15084v1 (pdf) |
| Date submitted: | Oct. 14, 2025, 4:27 p.m. |
| Submitted by: | Nathan Huetsch |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
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.
Author indications on fulfilling journal expectations
- Provide a novel and synergetic link between different research areas.
- Open a new pathway in an existing or a new research direction, with clear potential for multi-pronged follow-up work
- Detail a groundbreaking theoretical/experimental/computational discovery
- Present a breakthrough on a previously-identified and long-standing research stumbling block
Current status:
In refereeing
Reports on this Submission
Strengths
- The paper is well written, clearly elucidating the problem statement and the method adapted to solve that.
- The SPINUP method appears robust, and the details of how potential pitfalls were fixed is appreciated.
- Multiple examples, covering different physics processes have been presented to validate the method.
Weaknesses
- While the method itself seems well constructed, I have a concern about the applicability of it, please see the report.
Report
The paper addresses one of the classical problems in particle physics, the good old unfolding by using machine learning. While there have been a substantial body of work, the authors proposed a new approach. While the approach seems technically sound, it relies on minimising the difference between the detector level data and MC. This inherently assumes that the detector level MC is an accurate representation of the data. While in an ideal world, where MC modelling is perfect, this is fine, but we know MC mis-modelling is a real feature for many common processes and extreme phases spaces. Therefore this method will certainly introduce a bias, and since for many measurements (where unfolding is the most relevant) we want to probe MC mis-modelling, this bias is rather undesirable.
This drawback, unless I severely misunderstood the method limits the usefulness of the paper. However, I feel the method itself can be of use in other cases. Therefore I feel it is worth publishing once the authors clarify the above point, but probably Scipost Physics Core may be a better choice.
This drawback, unless I severely misunderstood the method limits the usefulness of the paper. However, I feel the method itself can be of use in other cases. Therefore I feel it is worth publishing once the authors clarify the above point, but probably Scipost Physics Core may be a better choice.
Requested changes
- Please address the above concern about the usefulness of the method as it seems to be based on minimising the difference with detector level MC.
- Abstract: the method is not just limited to the LHC, it can be useful for any collider experiment?
- I feel truth level or reco level (and in figure sim) reads like slang/shorthand, can the authors consistently use detector level, particle level, or parton level, as the case may be?
- In the sam vein, can the authors please not use part-level?
- "While NIS significantly eases its computational cost", can this be quantified?
- For the JSS dataset (section 4), it is known that the tune does affect the distributions. While for this proof of principle demonstration, it does not matter if the latest and the greatest tune has been used, please cite tune 26 (what tune is it?) and clarify what standard tune mains for H7.
Recommendation
Accept in alternative Journal (see Report)