mlsanityData to 14 Oct 2026

High Pileup Particle Tracking with Object Condensation

arXiv 2312.03823 ↗First posted 6 Dec 2023physics.data-an cs.LG hep-excited by 8

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Abstract (arXiv)

Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the HL-LHC. Most GNN tracking algorithms are based on edge classification and identify tracks as connected components from an initial graph containing spurious connections. In this talk, we consider an alternative based on object condensation (OC), a multi-objective learning framework designed to cluster points (hits) belonging to an arbitrary number of objects (tracks) and regress the properties of each object. Building on our previous results, we present a streamlined model and show progress toward a one-shot OC tracking algorithm in a high-pileup environment.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 6 Dec 2023.

Builds on

6 of 15 references.

References · 6

  1. 2016Interaction Networks for Learning about Objects, Relations and Physicscited by 509
  2. 2019Learning representations of irregular particle-detector geometry with distance-weighted graph networkscited by 43
  3. 2021Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Trackingcited by 30
  4. 2020Object condensation: one-stage grid-free multi-object reconstruction in physics detectors, graph and image datacited by 24
  5. 2021The Tracking Machine Learning challenge : Throughput phasecited by 12
  6. 2022End-to-end multi-particle reconstruction in high occupancy imaging calorimeters with graph neural networkscited by 6

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Cited by year (2026 to 14 Oct)

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

8 papers.

  1. 2024Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physicscited by 12
  2. 2024TrackFormers: In Search of Transformer-Based Particle Tracking for the High-Luminosity LHC Eracited by 4
  3. 2024EggNet: An Evolving Graph-based Graph Attention Network for Particle Track Reconstructioncited by 1
  4. 2025Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstructioncited by 1
  5. 2024Exploring End-to-end Differentiable Neural Charged Particle Tracking -- A Loss Landscape Perspectivecited by 0
  6. 2026Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectorscited by 0
  7. 2026HEPTv2: End-to-End Efficient Point Transformer for Charged Particle Reconstructioncited by 0
  8. 2026Using Line Segment Tracking to enable ML-based Track Reconstruction in CMScited by 0