mlsanityData to 7 Oct 2026

End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networks

arXiv 2204.01681 ↗First posted 4 Apr 2022physics.ins-det cs.CV cs.LGcited by 6

Not on the map. Why?

Abstract (arXiv)

We present an end-to-end reconstruction algorithm to build particle candidates from detector hits in next-generation granular calorimeters similar to that foreseen for the high-luminosity upgrade of the CMS detector. The algorithm exploits a distance-weighted graph neural network, trained with object condensation, a graph segmentation technique. Through a single-shot approach, the reconstruction task is paired with energy regression. We describe the reconstruction performance in terms of efficiency as well as in terms of energy resolution. In addition, we show the jet reconstruction performance of our method and discuss its inference computational cost. To our knowledge, this work is the first-ever example of single-shot calorimetric reconstruction of cal O(1000) particles in high-luminosity conditions with 200 pileup.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 42 references that are in the map. First posted on arXiv 4 Apr 2022.

Builds on

13 of 42 references.

References · 13

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2018Machine Learning in High Energy Physics Community White Papercited by 48
  3. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
  4. 2019Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physicscited by 42
  5. 2021MLPF: Efficient machine-learned particle-flow reconstruction using graph neural networkscited by 36
  6. 2020Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physicscited by 34
  7. 2021Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Trackingcited by 29
  8. 2020Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image datacited by 23
  9. 2021Charged particle tracking via edge-classifying interaction networkscited by 19
  10. 2020Track Seeding and Labelling with Embedded-space Graph Neural Networkscited by 12
  11. 2022Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challengescited by 12
  12. 2019End-to-End Jet Classification of Quarks and Gluons with the CMS Open Datacited by 11
1 more
  1. 2022Lightweight Jet Reconstruction and Identification as an Object Detection Taskcited by 1

Built on by

Cited by year

Citing papers per year: 2023: 3; 2024: 3.

6 papers.

  1. 2023High Pileup Particle Tracking with Object Condensationcited by 7
  2. 2024Unifying Simulation and Inference with Normalizing Flowscited by 6
  3. 2023Graph Structure from Point Clouds: Geometric Attention is All You Needcited by 2
  4. 2023The Optimal use of Segmentation for Sampling Calorimeterscited by 1
  5. 2024Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspectivecited by 0
  6. 2024Denoising Graph Super-Resolution towards Improved Collider Event Reconstructioncited by 0