Parametric neural networks learn likelihood ratios to infer top-philic scalar resonances from dip patterns caused by signal-background interference in hadron collider data.
Aad et al.,Differential tt cross-section measurements using boosted top quarks in the all-hadronic final state with 139 fb −1 of ATLAS data,JHEP04(2023) 080, [arXiv:2205.02817]
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Big Dipper, Help Me Find A Way -- Dip-hunting at hadron colliders
Parametric neural networks learn likelihood ratios to infer top-philic scalar resonances from dip patterns caused by signal-background interference in hadron collider data.