Factorizable Normalizing Flows represent parameter-dependent densities via a reference flow composed with a factorized polynomial transformation, enabling isolated per-parameter learning and linear scaling.
Neural networks for full phase-space reweighting and parameter tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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Differential cross sections for ttbar production in e+e-, mu+mu-, and e mu channels measured versus dineutrino pT and azimuthal separation using 138 fb-1 of 13 TeV CMS data, unfolded to particle level with unregularized least squares and found consistent with SM and MC predictions.
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Factorizable Normalizing Flows for parameter-dependent density morphing
Factorizable Normalizing Flows represent parameter-dependent densities via a reference flow composed with a factorized polynomial transformation, enabling isolated per-parameter learning and linear scaling.
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Measurement of the dineutrino system kinematic variables in dileptonic top quark pair production in proton-proton collisions at$\sqrt{s}$ = 13 TeV
Differential cross sections for ttbar production in e+e-, mu+mu-, and e mu channels measured versus dineutrino pT and azimuthal separation using 138 fb-1 of 13 TeV CMS data, unfolded to particle level with unregularized least squares and found consistent with SM and MC predictions.