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.
Differentiable MadNIS-Lite / T. Heimel, O. Mattelaer, T. Plehn, R. Winterhalder. - In: SCIPOST PHYSICS. - ISSN 2542-4653. - 18:1(2025), pp. 017.1-017.30. [10.21468/SciPostPhys.18.1.017]
Differentiable MadNIS-Lite
R. WinterhalderUltimo
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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