# Probing SMEFT Operators through tbarttbart Production with Hyper-Graph Neural Networks at the LHC

arXiv: https://arxiv.org/abs/2605.18382 · HTML: https://mlsanity.com/paper/2605.18382/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 40 references that are in the map.
- First posted on arXiv 18 May 2026.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Hypergraph Neural Networks](https://mlsanity.com/paper/1809.09401/) (2018)
- [Hypergraph Convolution and Hypergraph Attention](https://mlsanity.com/paper/1901.08150/) (2019)
- [Particle Transformer for Jet Tagging](https://mlsanity.com/paper/2202.03772/) (2022)
- [ParticleNet: Jet Tagging via Particle Clouds](https://mlsanity.com/paper/1902.08570/) (2019)
- [SPANet: Generalized Permutationless Set Assignment for Particle Physics using Symmetry Preserving Attention](https://mlsanity.com/paper/2106.03898/) (2021)
- [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://mlsanity.com/paper/2203.12852/) (2022)
- [Reconstructing short-lived particles using hypergraph representation learning](https://mlsanity.com/paper/2402.10149/) (2024)
- [From Information Geometry to Jet Substructure: A Triality of Cumulant Tensors, Energy Correlators, and Hypergraphs](https://mlsanity.com/paper/2605.03063/) (2026)

## Abstract (arXiv)

We present a phenomenological study of tbarttbart production in proton-proton collisions at √s = 13~TeV, using a Hyper-Graph Neural Network (H-GNN) to discriminate multilepton signal events from the dominant SM backgrounds, namely tbartW, tbartZ, tbartH, tbartVV, single-top associated production, and diboson and triboson processes. In the H-GNN architecture each event is represented as a hypergraph whose nodes correspond to reconstructed jets and leptons and whose hyperedges encode higher-order correlations among arbitrary subsets of these objects, allowing the network to learn the many-body kinematic structures that characterize the tbarttbart final state. Combining same-sign di-lepton, tri-lepton, and four-lepton channels following a CMS-like event selection, the H-GNN attains an area under the ROC curve of 0.951 for the tbarttbart signal and yields a statistical significance of Z = 9.11 at an integrated luminosity of L = 140~fb-1, to be compared with Z = 8.62 for a SPANet baseline, Z = 7.37 for a Particle Transformer baseline, and Z = 5.13 obtained by the ATLAS analysis, evaluated under identical event selection. We exploit the improved signal extraction to derive one- and two-parameter 95% confidence level limits on the Wilson coefficients of the dimension-six operators OPhi u, O(1)tt, O(1)qq, O(1)qt, and O(8)qt, and we project the expected sensitivity at the HL-LHC integrated luminosities of 1000~fb-1 and 3000~fb-1 with 50% uncertainty on the background estimation.