SciPost Submission Page
Generative Unfolding of Jets and Their Substructure
by Antoine Petitjean, Anja Butter, Kevin Greif, Sofia Palacios Schweitzer, Tilman Plehn, Jonas Spinner, Daniel Whiteson
Submission summary
| Authors (as registered SciPost users): | Antoine Petitjean · Tilman Plehn · Jonas Spinner |
| Submission information | |
|---|---|
| Preprint Link: | https://arxiv.org/abs/2510.19906v2 (pdf) |
| Code repository: | http://github.com/heidelberg-hepml/high-dim-unfolding |
| Date submitted: | Nov. 10, 2025, 5:55 p.m. |
| Submitted by: | Antoine Petitjean |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Computational, Phenomenological |
Abstract
Unfolding, for example of distortions imparted by detectors, provides suitable and publishable representations of LHC data. Many methods for unbinned and high-dimensional unfolding using machine learning have been proposed, but no generative method scales to the several hundred dimensions necessary to fully characterize LHC collisions. This paper proposes a 3-stage generative unfolding framework that is capable of unfolding several hundred dimensions. It is effective to unfold the jet-level kinematics as well as the full substructure of light-flavor jets and of top jets, and is the first generative unfolding study to achieve high precision on high-dimensional jet substructure.
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