Differentiable MadNIS-Lite

Heimel, Theo; Mattelaer, Olivier; Winterhalder, Ramon

doi:10.21468/SciPostPhys.18.1.017

Differentiable MadNIS-Lite

Heimel, Theo

;

Mattelaer, Olivier

;

Winterhalder, Ramon

;et.al.

(2025) SciPost Phys. — Vol. 18, n° 1, p. 7 (2025)

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Authors

Heimel, TheoUCLouvain
Author
Mattelaer, OlivierUCLouvain
Author
Winterhalder, RamonUCLouvain
Author
et. al.

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.

Affiliations

UCLouvainSST/IRMP - Institut de recherche en mathématique et physique

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Heimel, T., Mattelaer, O., Winterhalder, R., & et al. (2025). Differentiable MadNIS-Lite. SciPost Phys., 18(1), 7. https://doi.org/10.21468/SciPostPhys.18.1.017 (Original work published 2025)