Neural simulation-based inference on unbinned top-quark pair data at 13 TeV yields improved gluon PDF precision over traditional binned analyses while incorporating experimental and theoretical uncertainties.
Unbinned multivariate observables for global SMEFT analyses from machine learning
3 Pith papers cite this work, alongside 22 external citations. Polarity classification is still indexing.
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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.
The Minimum Resolution Likelihood method defines a fiducial signal region to convert ML-induced systematic effects into statistical uncertainties for unbiased signal strength estimation in collider analyses.
citing papers explorer
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Proton Structure from Neural Simulation-Based Inference at the LHC
Neural simulation-based inference on unbinned top-quark pair data at 13 TeV yields improved gluon PDF precision over traditional binned analyses while incorporating experimental and theoretical uncertainties.
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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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Defining a Minimum Resolution for Unbinned Analyses
The Minimum Resolution Likelihood method defines a fiducial signal region to convert ML-induced systematic effects into statistical uncertainties for unbiased signal strength estimation in collider analyses.