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Differentiable MadNIS-Lite

Theo Heimel, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder

SciPost Phys. 18, 017 (2025) · published 15 January 2025

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.


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