A GNN pretrained on 120M simulated HEP events generalizes to unseen processes and ATLAS data; fine-tuning boosts accuracy especially with small datasets, with CKA showing preserved encoders but altered intermediate layers.
Aadet al.(ATLAS), Observation of four-top-quark production in the multilepton final state with the AT- LAS detector, Eur
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A hyper-graph neural network improves discrimination of four-top production at 13 TeV, raising expected significance from 5.13 to 9.11 and enabling projected 95% CL limits on five dimension-six SMEFT Wilson coefficients at current and HL-LHC luminosities.
ATLAS reports a measurement of high-mass ttbar ll production with no observed deviation from the Standard Model and derives EFT constraints on four-fermion operators from 140 fb^{-1} of Run 2 data.
citing papers explorer
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Pretrained Event Classification Model for High Energy Physics Analysis
A GNN pretrained on 120M simulated HEP events generalizes to unseen processes and ATLAS data; fine-tuning boosts accuracy especially with small datasets, with CKA showing preserved encoders but altered intermediate layers.
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Probing SMEFT Operators through $t\bar{t}t\bar{t}$ Production with Hyper-Graph Neural Networks at the LHC
A hyper-graph neural network improves discrimination of four-top production at 13 TeV, raising expected significance from 5.13 to 9.11 and enabling projected 95% CL limits on five dimension-six SMEFT Wilson coefficients at current and HL-LHC luminosities.
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Measurement of high-mass $t\bar{t}\ell^{+}\ell^{-}$ production and lepton flavour universality-inspired effective field theory interpretations at $\sqrt{s}=13$ TeV with the ATLAS detector
ATLAS reports a measurement of high-mass ttbar ll production with no observed deviation from the Standard Model and derives EFT constraints on four-fermion operators from 140 fb^{-1} of Run 2 data.