SciPost Submission Page
The Physics Behind ML-based Quark-Gluon Taggers
by Sophia Vent, Ramon Winterhalder, Tilman Plehn
Submission summary
| Authors (as registered SciPost users): | Tilman Plehn · Sophia Vent · Ramon Winterhalder |
| Submission information | |
|---|---|
| Preprint Link: | scipost_202512_00047v1 (pdf) |
| Date submitted: | Dec. 23, 2025, 3:41 p.m. |
| Submitted by: | Ramon Winterhalder |
| Submitted to: | SciPost Physics |
| Ontological classification | |
|---|---|
| Academic field: | Physics |
| Specialties: |
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| Approaches: | Theoretical, Computational |
Abstract
Jet taggers provide an ideal testbed for applying explainability techniques to powerful ML tools. For theoretically and experimentally challenging quark-gluon tagging, we first identify the leading latent features that correlate strongly with physics observables, both in a linear and a non-linear approach. Next, we show how Shapley values can assess feature importance, although the standard implementation assumes independent inputs and can lead to distorted attributions in the presence of correlations. Finally, we use symbolic regression to derive compact formulas to approximate the tagger output.
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
List of changes
- We clarified the scope of the paper.
- We toned down several claims and revised the title accordingly.
- We added a new study and analysis of energy flow polynomials.
- We included a mutual information analysis.
- We corrected minor mistakes and typographical errors.
- We addressed all referee comments individually and incorporated their suggestions.
Current status:
Refereeing in preparation