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.
Data-Driven High-Dimensional Statistical Inference with Generative Models.JHEP, 11:129, 2025
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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.
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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.