# Progress towards an improved particle flow algorithm at CMS with machine learning

arXiv: https://arxiv.org/abs/2303.17657 · HTML: https://mlsanity.com/paper/2303.17657/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 30 Mar 2023.

## Builds on (most cited first, 5 of 5)

- [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://mlsanity.com/paper/2101.08578/) (2021)
- [Towards a Computer Vision Particle Flow](https://mlsanity.com/paper/2003.08863/) (2020)
- [Machine Learning for Particle Flow Reconstruction at CMS](https://mlsanity.com/paper/2203.00330/) (2022)
- [Explaining machine-learned particle-flow reconstruction](https://mlsanity.com/paper/2111.12840/) (2021)
- [Hyperparameter optimization of data-driven AI models on HPC systems](https://mlsanity.com/paper/2203.01112/) (2022)

## Built on by (most cited first, 7 of 7)

- [Fine-tuning machine-learned particle-flow reconstruction for new detector geometries in future colliders](https://mlsanity.com/paper/2503.00131/) (2025)
- [Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors](https://mlsanity.com/paper/2309.06782/) (2023)
- [PASCL: Supervised Contrastive Learning with Perturbative Augmentation for Particle Decay Reconstruction](https://mlsanity.com/paper/2402.11538/) (2024)
- [End-to-end event reconstruction for precision physics at future colliders](https://mlsanity.com/paper/2603.04084/) (2026)
- [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)

The particle-flow (PF) algorithm, which infers particles based on tracks and calorimeter clusters, is of central importance to event reconstruction in the CMS experiment at the CERN LHC, and has been a focus of development in light of planned Phase-2 running conditions with an increased pileup and detector granularity. In recent years, the machine learned particle-flow (MLPF) algorithm, a graph neural network that performs PF reconstruction, has been explored in CMS, with the possible advantages of directly optimizing for the physical quantities of interest, being highly reconfigurable to new conditions, and being a natural fit for deployment to heterogeneous accelerators. We discuss progress in CMS towards an improved implementation of the MLPF reconstruction, now optimized using generator/simulation-level particle information as the target for the first time. This paves the way to potentially improving the detector response in terms of physical quantities of interest. We describe the simulation-based training target, progress and studies on event-based loss terms, details on the model hyperparameter tuning, as well as physics validation with respect to the current PF algorithm in terms of high-level physical quantities such as the jet and missing transverse momentum resolutions. We find that the MLPF algorithm, trained on a generator/simulator level particle information for the first time, results in broadly compatible particle and jet reconstruction performance with the baseline PF, setting the stage for improving the physics performance by additional training statistics and model tuning.