Differentiable MadNIS-Lite

TL;DR

Differentiable MadNIS-Lite introduces a new, physically interpretable sampling strategy for particle physics simulations that enhances efficiency by combining phase-space mappings with learnable flow elements, complementing existing methods.

Contribution

It presents MadNIS-Lite, a novel differentiable sampling method that improves efficiency and interpretability in particle physics event generation.

Findings

Enhanced sampling efficiency demonstrated

Physically interpretable flow elements incorporated

Complementary to existing VEGAS and MadNIS methods

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.