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arXiv · 2408.01486

Differentiable MadNIS-Lite

Theo Heimel·Olivier Mattelaer·Tilman Plehn·Ramon Winterhalder
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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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Theo Heimel, Olivier Mattelaer, Tilman Plehn, Ramon Winterhalder. 2024-08-02. Differentiable MadNIS-Lite. https://doi.org/10.21468/scipostphys.18.1.017

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