Differentiable MadNIS-Lite
Theo Heimel, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder
Abstract
Differentiable programming opens exciting new avenues in particle physics, also affecting future event generators. These new techniques boost the performance of current and planned MadGraph implementations. Combining phase-space mappings with a set of very small learnable flow elements, MADNIS-Lite, can improve the sampling efficiency while being physically interpretable. This defines a third sampling strategy, complementing VEGAS and the full MADNIS.
Topics & Concepts
Differentiable functionGeologyComputer scienceMathematicsPure mathematicsParticle physics theoretical and experimental studiesAlgebraic and Geometric AnalysisParticle Detector Development and Performance