# Towards a Computer Vision Particle Flow

arXiv: https://arxiv.org/abs/2003.08863 · HTML: https://mlsanity.com/paper/2003.08863/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2020.

## Built on by (most cited first, 15 of 15)

- [MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networks](https://mlsanity.com/paper/2101.08578/) (2021)
- [Generative Networks for Precision Enthusiasts](https://mlsanity.com/paper/2110.13632/) (2021)
- [Understanding Event-Generation Networks via Uncertainties](https://mlsanity.com/paper/2104.04543/) (2021)
- [Machine Learning for Particle Flow Reconstruction at CMS](https://mlsanity.com/paper/2203.00330/) (2022)
- [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)
- [Calorimeter shower superresolution](https://mlsanity.com/paper/2308.11700/) (2023)
- [SR-GAN for SR-gamma: super resolution of photon calorimeter images at collider experiments](https://mlsanity.com/paper/2308.09025/) (2023)
- [Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectors](https://mlsanity.com/paper/2309.06782/) (2023)
- [Configurable calorimeter simulation for AI applications](https://mlsanity.com/paper/2303.02101/) (2023)
- [End-to-end event reconstruction for precision physics at future colliders](https://mlsanity.com/paper/2603.04084/) (2026)
- [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)

In High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the complementary measurements of charged particle tracking devices, to provide a superior measurement of the particle content and kinematics. In this paper, a computer vision approach to this fundamental aspect of PFlow algorithms, based on calorimeter images, is proposed. A comparative study of the state of the art deep learning techniques is performed. A significantly improved reconstruction of the neutral particle calorimeter energy deposits is obtained in a context of large overlaps with the deposits from charged particles. Calorimeter images with augmented finer granularity are also obtained using super-resolution techniques.