FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Dittmaier,et al.JHEP11(2023), 022 [arXiv:2308.16716 [hep-ph]]
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Spin-correlation asymmetries in same-sign WW production yield sensitivity to anomalous WWWW couplings comparable to transverse-mass distributions, and their combination improves Wilson-coefficient limits while respecting unitarity cuts.
Electroweak corrections reach about -16% for like-sign WW scattering and -7% for triple-W production at the LHC, even for integrated cross sections, and the paper discusses approximations to full off-shell calculations.
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Local Conformal Predictions for Calibrated Surrogates
FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
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Probing anomalous quartic gauge couplings in same-sign $W$ boson scattering with polarization and spin correlation
Spin-correlation asymmetries in same-sign WW production yield sensitivity to anomalous WWWW couplings comparable to transverse-mass distributions, and their combination improves Wilson-coefficient limits while respecting unitarity cuts.
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Precision calculations for electroweak multi-boson processes
Electroweak corrections reach about -16% for like-sign WW scattering and -7% for triple-W production at the LHC, even for integrated cross sections, and the paper discusses approximations to full off-shell calculations.