FALCON is a novel conformal prediction technique that learns locally calibrated confidence intervals for neural network surrogates modeling LHC scattering amplitudes.
Returning CP-observables to the frames they belong
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RANs generalize moment unfolding to full phase-space unbinned unfolding via detector-level Wasserstein critics without requiring support overlap or multiple iterations.
Computes entanglement negativities for the tripartite spin system in e+e- -> ttZ and projects that collective entanglement is accessible but genuine multipartite entanglement has limited sensitivity at a polarized ILC with expected luminosity.
Semi-leptonic h to VV* decays retain an effective two-qutrit description for quantum tomography and entanglement after including finite fermion masses and NLO corrections.
citing papers explorer
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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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Quantum Tomography and Entanglement in Semi-Leptonic $h\to VV^*$ Decays at Higher Orders
Semi-leptonic h to VV* decays retain an effective two-qutrit description for quantum tomography and entanglement after including finite fermion masses and NLO corrections.