MadSpace -- Event Generation for the Era of GPUs and ML
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910030979137536 |
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| author | Heimel, Theo Mattelaer, Olivier Winterhalder, Ramon |
| author_facet | Heimel, Theo Mattelaer, Olivier Winterhalder, Ramon |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06895 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MadSpace -- Event Generation for the Era of GPUs and ML Heimel, Theo Mattelaer, Olivier Winterhalder, Ramon High Energy Physics - Phenomenology 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. |
| title | MadSpace -- Event Generation for the Era of GPUs and ML |
| topic | High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2602.06895 |