mlsanityData to 7 Oct 2026

Machine Learning for Particle Flow Reconstruction at CMS

arXiv 2203.00330 ↗First posted 1 Mar 2022physics.data-an cs.LG hep-excited by 14

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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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 12 of its 55 references that are in the map. First posted on arXiv 1 Mar 2022.

Builds on

12 of 55 references.

References · 12

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  4. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 503
  5. 2018BOHB: Robust and Efficient Hyperparameter Optimization at Scalecited by 466
  6. 2018A System for Massively Parallel Hyperparameter Tuningcited by 201
  7. 2019ParticleNet: Jet Tagging via Particle Cloudscited by 64
  8. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
  9. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  10. 2020Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image datacited by 23
  11. 2020Towards a Computer Vision Particle Flowcited by 15
  12. 2020Track Seeding and Labelling with Embedded-space Graph Neural Networkscited by 12

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 4; 2025: 2; 2026: 1.

14 papers.

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  1. 2024Finetuning Foundation Models for Joint Analysis Optimizationcited by 18
  2. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  3. 2024A Language Model for Particle Trackingcited by 10
  4. 2025Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliderscited by 8
  5. 2023Progress towards an improved particle flow algorithm at CMS with machine learningcited by 7
  6. 2022Do graph neural networks learn traditional jet substructure?cited by 4
  7. 2023Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectorscited by 4
  8. 2022Hyperparameter optimization of data-driven AI models on HPC systemscited by 3
  9. 2023Efficient and Robust Jet Tagging at the LHC with Knowledge Distillationcited by 3
  10. 2026End-to-end event reconstruction for precision physics at future colliderscited by 2
  11. 2023Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPCcited by 1
  12. 2025Machine-learning based particle-flow algorithm in CMScited by 1
2 more
  1. 2024Denoising Graph Super-Resolution towards Improved Collider Event Reconstructioncited by 0
  2. 2024Lightweight Deep Learning Framework for Accurate Particle Flow Energy Reconstructioncited by 0