Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction

• URL: http://arxiv.org/abs/2510.07594v1
• Date: Wed, 08 Oct 2025 22:36:26 GMT
• Title: Locality-Sensitive Hashing-Based Efficient Point Transformer for Charged Particle Reconstruction
• Authors: Shitij Govil, Jack P. Rodgers, Yuan-Tang Chou, Siqi Miao, Amit Saha, Advaith Anand, Kilian Lieret, Gage DeZoort, Mia Liu, Javier Duarte, Pan Li, Shih-Chieh Hsu,
• Abstract summary: We present a unified, fair evaluation of physics tracking performance for HEPT and a representative GNN-based pipeline.<n>We introduce HEPTv2 by extending HEPT with a lightweight decoder that eliminates the clustering stage and directly predicts track assignments.<n>On the TrackML dataset, optimized HEPTv2 achieves approximately 28 ms per event on an A100 while maintaining competitive tracking efficiency.
• Score: 9.405982649278437
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Charged particle track reconstruction is a foundational task in collider experiments and the main computational bottleneck in particle reconstruction. Graph neural networks (GNNs) have shown strong performance for this problem, but costly graph construction, irregular computations, and random memory access patterns substantially limit their throughput. The recently proposed Hashing-based Efficient Point Transformer (HEPT) offers a theoretically guaranteed near-linear complexity for large point cloud processing via locality-sensitive hashing (LSH) in attention computations; however, its evaluations have largely focused on embedding quality, and the object condensation pipeline on which HEPT relies requires a post-hoc clustering step (e.g., DBScan) that can dominate runtime. In this work, we make two contributions. First, we present a unified, fair evaluation of physics tracking performance for HEPT and a representative GNN-based pipeline under the same dataset and metrics. Second, we introduce HEPTv2 by extending HEPT with a lightweight decoder that eliminates the clustering stage and directly predicts track assignments. This modification preserves HEPT's regular, hardware-friendly computations while enabling ultra-fast end-to-end inference. On the TrackML dataset, optimized HEPTv2 achieves approximately 28 ms per event on an A100 while maintaining competitive tracking efficiency. These results position HEPTv2 as a practical, scalable alternative to GNN-based pipelines for fast tracking.

Related papers

• GSPN-2: Efficient Parallel Sequence Modeling [101.33780567131716]
  Generalized Spatial Propagation Network (GSPN) addresses this by replacing quadratic self-attention with a line-scan propagation scheme.<n>GSPN-2 establishes a new efficiency frontier for modeling global spatial context in vision applications.
  arXiv  Detail & Related papers  (2025-11-28T07:26:45Z)
• Scaling Graph Neural Networks for Particle Track Reconstruction [0.0]
  We introduce improvements to the Exa.TrkX pipeline to train on samples of input particle graphs.<n>We adapt performance optimizations, introduced for GNN training, to fit our augmented Exa.TrkX pipeline.
  arXiv  Detail & Related papers  (2025-04-07T01:44:32Z)
• Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics [11.182510067821745]
  This study introduces a novel transformer model optimized for large-scale point cloud processing. Our model integrates local inductive bias and achieves near-linear complexity with hardware-friendly regular operations. Our findings highlight the superiority of using locality-sensitive hashing (LSH), especially OR & AND-construction LSH, in kernel approximation for large-scale point cloud data.
  arXiv  Detail & Related papers  (2024-02-19T20:48:09Z)
• Edge-Enabled Real-time Railway Track Segmentation [0.0]
  We propose an edge-enabled real-time railway track segmentation algorithm. It is optimized to be suitable for edge applications by optimizing the network structure and quantizing the model after training. Experimental results demonstrate that our enhanced algorithm achieves an accuracy level of 83.3%.
  arXiv  Detail & Related papers  (2024-01-21T13:45:52Z)
• Efficient Heterogeneous Graph Learning via Random Projection [58.4138636866903]
  Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Recent pre-computation-based HGNNs use one-time message passing to transform a heterogeneous graph into regular-shaped tensors. We propose a hybrid pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN)
  arXiv  Detail & Related papers  (2023-10-23T01:25:44Z)
• Efficient Dataset Distillation Using Random Feature Approximation [109.07737733329019]
  We propose a novel algorithm that uses a random feature approximation (RFA) of the Neural Network Gaussian Process (NNGP) kernel. Our algorithm provides at least a 100-fold speedup over KIP and can run on a single GPU. Our new method, termed an RFA Distillation (RFAD), performs competitively with KIP and other dataset condensation algorithms in accuracy over a range of large-scale datasets.
  arXiv  Detail & Related papers  (2022-10-21T15:56:13Z)
• LL-GNN: Low Latency Graph Neural Networks on FPGAs for High Energy Physics [45.666822327616046]
  This work presents a novel reconfigurable architecture for Low Graph Neural Network (LL-GNN) designs for particle detectors. The LL-GNN design advances the next generation of trigger systems by enabling sophisticated algorithms to process experimental data efficiently.
  arXiv  Detail & Related papers  (2022-09-28T12:55:35Z)
• Lightweight Jet Reconstruction and Identification as an Object Detection Task [5.071565475111431]
  We apply convolutional techniques to end-to-end jet identification and reconstruction tasks encountered at the CERN Large Hadron Collider. PFJet-SSD performs simultaneous localization, classification and regression tasks to cluster jets and reconstruct their features. We show that the ternary network closely matches the performance of its full-precision equivalent and outperforms the state-of-the-art rule-based algorithm.
  arXiv  Detail & Related papers  (2022-02-09T15:01:53Z)
• FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation [81.76975488010213]
  Dense optical flow estimation plays a key role in many robotic vision tasks. Current networks often occupy large number of parameters and require heavy computation costs. Our proposed FastFlowNet works in the well-known coarse-to-fine manner with following innovations.
  arXiv  Detail & Related papers  (2021-03-08T03:09:37Z)
• Distance-Weighted Graph Neural Networks on FPGAs for Real-Time Particle Reconstruction in High Energy Physics [11.125632758828266]
  We discuss how to design distance-weighted graph networks that can be executed with a latency of less than 1$mumathrms$ on an FPGA. We consider a representative task associated to particle reconstruction and identification in a next-generation calorimeter operating at a particle collider. We convert the compressed models into firmware to be implemented on an FPGA.
  arXiv  Detail & Related papers  (2020-08-08T21:26:31Z)
• Learning to Optimize Non-Rigid Tracking [54.94145312763044]
  We employ learnable optimizations to improve robustness and speed up solver convergence. First, we upgrade the tracking objective by integrating an alignment data term on deep features which are learned end-to-end through CNN. Second, we bridge the gap between the preconditioning technique and learning method by introducing a ConditionNet which is trained to generate a preconditioner.
  arXiv  Detail & Related papers  (2020-03-27T04:40:57Z)
• Learning to Hash with Graph Neural Networks for Recommender Systems [103.82479899868191]
  Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. We propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
  arXiv  Detail & Related papers  (2020-03-04T06:59:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.