MadSpace -- Event Generation for the Era of GPUs and ML

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Authors: Theo Heimel, Olivier Mattelaer, Ramon Winterhalder
Cited by: 0

Abstract

MadSpace is a new modular phase-space and event-generation library written in C++ with native GPU support via CUDA and HIP. It provides a unified compute-graph-based framework for phase-space construction, adaptive and neural importance sampling, and event unweighting. It includes a wide range of mappings, from the standard MadGraph multi-channel phase space to optimized normalizing flows with analytic inverse transformations. All components operate on batches of events and support end-to-end on-device workflows. A high-level Python interface enables seamless integration with machine-learning libraries such as PyTorch.

Fields of Study & Subjects

Particle physics theoretical and experimental studies Parallel Computing and Optimization Techniques Computational Physics and Python Applications

Publication details

Journal / source
arXiv (Cornell University)
Publication date
6 February 2026
Document type
Preprint