ML-based particle flow (MLPF) focuses on developing full event reconstruction for particle detectors using computationally scalable and flexible machine learning models. The project aims to improve particle flow reconstruction across various detector environments, including CMS, as well as future detectors via Key4HEP. We build on existing, open-souce simulation software by the experimental collaborations.
Below is the development timeline of MLPF by our team, ranging from initial proofs of concept to full detector simulations and fine-tuning studies.
2021: First full-event GNN demonstration of MLPF
- Paper: MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks (Eur. Phys. J. C)
- Focus: Initial idea with a scalable GNN.
- Code: v1.1
- Dataset: Zenodo Record
2021: First demonstration in CMS Run 3
- Paper: Machine Learning for Particle Flow Reconstruction at CMS (J. Phys. Conf. Ser.)
- Note: CERN-CMS-DP-2021-030
2022: Improved performance in CMS Run 3
- Note: CERN-CMS-DP-2022-061
2024: Improved performance with CLIC full simulation
- Paper: MLPF: efficient machine-learned particle-flow reconstruction using graph neural networks (Communications Physics)
- Focus: Improved event-level performance in full simulation.
- Code: v1.6.2
- Results: Zenodo Record
2025: Fine-tuning across detectors
- Paper (Fine-tuning): Fine-tuning from CLIC to CLD (Phys. Rev. D)
- Code: v2.3.0
2025/2026: CMS Run 3 full results
- Note (EPS-HEP 2025): CERN-CMS-DP-2025-033
- Paper: CMS Run 3 paper (arXiv, submitted to EPJC)
- Code: v2.4.0
Please ensure you use the correct version of the jpata/particleflow software with the corresponding dataset version.
| Code Version | CMS Dataset | CLIC Dataset | CLD Dataset |
|---|---|---|---|
| 1.9.0 | 2.4.0 | 2.2.0 | NA |
| 2.0.0 | 2.4.0 | 2.3.0 | NA |
| 2.1.0 | 2.5.0 | 2.5.0 | NA |
| 2.2.0 | 2.5.0 | 2.5.0 | 2.5.0 |
| 2.3.0 | 2.5.0 | 2.5.0 | 2.5.0 |
| 2.4.0 | 2.6.0 | 2.5.0 | 2.5.0 |
You are welcome to reuse the code in accordance with the LICENSE.
How to Cite
- Academic Work: Please cite the specific papers listed in the Publications section above relevant to the method you are using (e.g., initial GNN idea, fine-tuning, or specific detector studies).
- Code Usage: If you use the code significantly for research, please cite the specific tagged version from Zenodo.
- Dataset Usage: Cite the appropriate dataset via the Zenodo link and the corresponding paper.
Contact
For collaboration ideas that do not fit into the categories above, please get in touch via GitHub Discussions.