# Machine Learning for Particle Flow Reconstruction at CMS

arXiv: https://arxiv.org/abs/2203.00330 · HTML: https://mlsanity.com/paper/2203.00330/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 55 references that are in the map.
- First posted on arXiv 1 Mar 2022.

## Builds on (most cited first, 12 of 12)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Interaction Networks for Learning about Objects, Relations and Physics](https://mlsanity.com/paper/1612.00222/) (2016)
- [BOHB: Robust and Efficient Hyperparameter Optimization at Scale](https://mlsanity.com/paper/1807.01774/) (2018)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [ParticleNet: Jet Tagging via Particle Clouds](https://mlsanity.com/paper/1902.08570/) (2019)
- [Learning representations of irregular particle-detector geometry with distance-weighted graph networks](https://mlsanity.com/paper/1902.07987/) (2019)
- [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://mlsanity.com/paper/2101.08578/) (2021)
- [Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image data](https://mlsanity.com/paper/2002.03605/) (2020)
- [Towards a Computer Vision Particle Flow](https://mlsanity.com/paper/2003.08863/) (2020)
- [Track Seeding and Labelling with Embedded-space Graph Neural Networks](https://mlsanity.com/paper/2007.00149/) (2020)

## Built on by (most cited first, 14 of 14)

- [Finetuning Foundation Models for Joint Analysis Optimization](https://mlsanity.com/paper/2401.13536/) (2024)
- [Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges](https://mlsanity.com/paper/2203.12852/) (2022)
- [A Language Model for Particle Tracking](https://mlsanity.com/paper/2402.10239/) (2024)
- [Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders](https://mlsanity.com/paper/2503.00131/) (2025)
- [Progress towards an improved particle flow algorithm at CMS with machine learning](https://mlsanity.com/paper/2303.17657/) (2023)
- [Do graph neural networks learn traditional jet substructure?](https://mlsanity.com/paper/2211.09912/) (2022)
- [Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors](https://mlsanity.com/paper/2309.06782/) (2023)
- [Hyperparameter optimization of data-driven AI models on HPC systems](https://mlsanity.com/paper/2203.01112/) (2022)
- [Efficient and Robust Jet Tagging at the LHC with Knowledge Distillation](https://mlsanity.com/paper/2311.14160/) (2023)
- [End-to-end event reconstruction for precision physics at future colliders](https://mlsanity.com/paper/2603.04084/) (2026)
- [Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC](https://mlsanity.com/paper/2303.15053/) (2023)
- [Machine-learning based particle-flow algorithm in CMS](https://mlsanity.com/paper/2508.20541/) (2025)
- [Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction](https://mlsanity.com/paper/2409.16052/) (2024)
- [Lightweight Deep Learning Framework for Accurate Particle Flow Energy Reconstruction](https://mlsanity.com/paper/2410.07250/) (2024)

## Abstract (arXiv)

We provide details on the implementation of a machine-learning based particle flow algorithm for CMS. The standard particle flow algorithm reconstructs stable particles based on calorimeter clusters and tracks to provide a global event reconstruction that exploits the combined information of multiple detector subsystems, leading to strong improvements for quantities such as jets and missing transverse energy. We have studied a possible evolution of particle flow towards heterogeneous computing platforms such as GPUs using a graph neural network. The machine-learned PF model reconstructs particle candidates based on the full list of tracks and calorimeter clusters in the event. For validation, we determine the physics performance directly in the CMS software framework when the proposed algorithm is interfaced with the offline reconstruction of jets and missing transverse energy. We also report the computational performance of the algorithm, which scales approximately linearly in runtime and memory usage with the input size.