Charged particle tracking via edge-classifying interaction networks

• URL: http://arxiv.org/abs/2103.16701v1
• Date: Tue, 30 Mar 2021 21:58:52 GMT
• Title: Charged particle tracking via edge-classifying interaction networks
• Authors: Gage DeZoort, Savannah Thais, Isobel Ojalvo, Peter Elmer, Vesal Razavimaleki, Javier Duarte, Markus Atkinson, Mark Neubauer
• Abstract summary: In this work, we adapt the physics-motivated interaction network (IN) GNN to the problem of charged-particle tracking in the high-pileup conditions expected at the HL-LHC. We demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures, a reduction in size critical for enabling GNN-based tracking in constrained computing environments.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well-suited to address a variety of reconstruction problems in HEP. In particular, tracker events are naturally represented as graphs by identifying hits as nodes and track segments as edges; given a set of hypothesized edges, edge-classifying GNNs predict which correspond to real track segments. In this work, we adapt the physics-motivated interaction network (IN) GNN to the problem of charged-particle tracking in the high-pileup conditions expected at the HL-LHC. We demonstrate the IN's excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures, a reduction in size critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN is easily expressed as a set of matrix operations, making it a promising candidate for acceleration via heterogeneous computing resources.

Related papers

• High Pileup Particle Tracking with Object Condensation [7.962871190916326]
  Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking. 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.
  arXiv  Detail & Related papers  (2023-12-06T19:00:00Z)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• MentorGNN: Deriving Curriculum for Pre-Training GNNs [61.97574489259085]
  We propose an end-to-end model named MentorGNN that aims to supervise the pre-training process of GNNs across graphs. We shed new light on the problem of domain adaption on relational data (i.e., graphs) by deriving a natural and interpretable upper bound on the generalization error of the pre-trained GNNs.
  arXiv  Detail & Related papers  (2022-08-21T15:12:08Z)
• Robust Graph Neural Networks using Weighted Graph Laplacian [1.8292714902548342]
  Graph neural network (GNN) is vulnerable to noise and adversarial attacks in input data. We propose a generic framework for robustifying GNN known as Weighted Laplacian GNN (RWL-GNN)
  arXiv  Detail & Related papers  (2022-08-03T05:36:35Z)
• Graph Neural Networks for Charged Particle Tracking on FPGAs [2.6402980149746913]
  The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem. Graph neural networks (GNNs) are a type of geometric deep learning algorithm that has successfully been applied to this task. We introduce an automated translation workflow, integrated into a broader tool called $textthls4ml$, for converting GNNs into firmware for field-programmable gate arrays (FPGAs)
  arXiv  Detail & Related papers  (2021-12-03T17:56:10Z)
• Edge-Level Explanations for Graph Neural Networks by Extending Explainability Methods for Convolutional Neural Networks [33.20913249848369]
  Graph Neural Networks (GNNs) are deep learning models that take graph data as inputs, and they are applied to various tasks such as traffic prediction and molecular property prediction. We extend explainability methods for CNNs, such as Local Interpretable Model-Agnostic Explanations (LIME), Gradient-Based Saliency Maps, and Gradient-Weighted Class Activation Mapping (Grad-CAM) to GNNs. The experimental results indicate that the LIME-based approach is the most efficient explainability method for multiple tasks in the real-world situation, outperforming even the state-of-the
  arXiv  Detail & Related papers  (2021-11-01T06:27:29Z)
• ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network [72.16255675586089]
  We propose an Adaptive Curvature Exploration Hyperbolic Graph NeuralNetwork named ACE-HGNN to adaptively learn the optimal curvature according to the input graph and downstream tasks. Experiments on multiple real-world graph datasets demonstrate a significant and consistent performance improvement in model quality with competitive performance and good generalization ability.
  arXiv  Detail & Related papers  (2021-10-15T07:18:57Z)
• Learning to Drop: Robust Graph Neural Network via Topological Denoising [50.81722989898142]
  We propose PTDNet, a parameterized topological denoising network, to improve the robustness and generalization performance of Graph Neural Networks (GNNs) PTDNet prunes task-irrelevant edges by penalizing the number of edges in the sparsified graph with parameterized networks. We show that PTDNet can improve the performance of GNNs significantly and the performance gain becomes larger for more noisy datasets.
  arXiv  Detail & Related papers  (2020-11-13T18:53:21Z)
• Track Seeding and Labelling with Embedded-space Graph Neural Networks [3.5236955190576693]
  The Exa.TrkX project is investigating machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event as a graph that connects track measurements. We report updates on the state-of-the-art architectures for this task.
  arXiv  Detail & Related papers  (2020-06-30T23:43:28Z)
• Graph Neural Networks for Motion Planning [108.51253840181677]
  We present two techniques, GNNs over dense fixed graphs for low-dimensional problems and sampling-based GNNs for high-dimensional problems. We examine the ability of a GNN to tackle planning problems such as identifying critical nodes or learning the sampling distribution in Rapidly-exploring Random Trees (RRT) Experiments with critical sampling, a pendulum and a six DoF robot arm show GNNs improve on traditional analytic methods as well as learning approaches using fully-connected or convolutional neural networks.
  arXiv  Detail & Related papers  (2020-06-11T08:19:06Z)
• Binarized Graph Neural Network [65.20589262811677]
  We develop a binarized graph neural network to learn the binary representations of the nodes with binary network parameters. Our proposed method can be seamlessly integrated into the existing GNN-based embedding approaches. Experiments indicate that the proposed binarized graph neural network, namely BGN, is orders of magnitude more efficient in terms of both time and space.
  arXiv  Detail & Related papers  (2020-04-19T09:43:14Z)

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.