The DOI matches its Crossref record.
Constraining Effective Field Theories with Machine Learning
Open accessAbstract
We present powerful new analysis techniques to constrain effective field theories at the LHC. By leveraging the structure of particle physics processes, we extract extra information from Monte Carlo simulations, which can be used to train neural network models that estimate the likelihood ratio. These methods scale well to processes with many observables and theory parameters, do not require any approximations of the parton shower or detector response, and can be evaluated in microseconds. We show that they allow us to put significantly stronger bounds on dimension-six operators than existing methods, demonstrating their potential to improve the precision of the LHC legacy constraints.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
The frontier of simulation-based inference
Cranmer, K., Brehmer, J., & Louppe, G. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1912789117 · Cited by 1,032
Self-citationPiscopo, M. L., Spannowsky, M., & Waite, P. (2019) · Physical Review D · doi:10.1103/physrevd.100.016002 · Cited by 117
Brehmer, J., Mishra-Sharma, S., Hermans, J., Louppe, G., & Cranmer, K. (2019) · The Astrophysical Journal · doi:10.3847/1538-4357/ab4c41 · Cited by 80
Self-citation
Metadata
- Authors
- Brehmer, J., Cranmer, K., Louppe, G., & Pavez, J.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2018-09-12
- Volume
- 121
- Issue
- 11
- Pages
- 111801-111801
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2798398359
- Cited by
- 160
- References
- 0
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Open access
- hybrid
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Constraining Effective Field Theories with Machine Learning. https://app.cochat.ai/papers/10.1103/physrevlett.121.111801
- The paper
- Brehmer, J., Cranmer, K., Louppe, G., & Pavez, J. (2018). Constraining Effective Field Theories with Machine Learning. Physical Review Letters. https://doi.org/10.1103/physrevlett.121.111801
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
