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.
@article{arxiv.2602.06895,
title = {MadSpace -- Event Generation for the Era of GPUs and ML},
author = {Theo Heimel and Olivier Mattelaer and Ramon Winterhalder},
journal= {arXiv preprint arXiv:2602.06895},
year = {2026}
}