Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems

• URL: http://arxiv.org/abs/2507.19205v1
• Date: Fri, 25 Jul 2025 12:19:57 GMT
• Title: Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
• Authors: Md Abrar Jahin, Shahriar Soudeep, M. F. Mridha, Muhammad Mostafa Monowar, Md. Abdul Hamid,
• Abstract summary: Real-time particle transverse momentum ($p_T$) estimation in high-energy physics demands efficient algorithms under strict hardware constraints.<n>We propose a physics-informed graph neural network (GNN) framework that systematically encodes detector geometry and physical observables.<n>Our co-design methodology yields superior accuracy-efficiency trade-offs compared to existing baselines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Real-time particle transverse momentum ($p_T$) estimation in high-energy physics demands algorithms that are both efficient and accurate under strict hardware constraints. Static machine learning models degrade under high pileup and lack physics-aware optimization, while generic graph neural networks (GNNs) often neglect domain structure critical for robust $p_T$ regression. We propose a physics-informed GNN framework that systematically encodes detector geometry and physical observables through four distinct graph construction strategies that systematically encode detector geometry and physical observables: station-as-node, feature-as-node, bending angle-centric, and pseudorapidity ($\eta$)-centric representations. This framework integrates these tailored graph structures with a novel Message Passing Layer (MPL), featuring intra-message attention and gated updates, and domain-specific loss functions incorporating $p_{T}$-distribution priors. Our co-design methodology yields superior accuracy-efficiency trade-offs compared to existing baselines. Extensive experiments on the CMS Trigger Dataset validate the approach: a station-informed EdgeConv model achieves a state-of-the-art MAE of 0.8525 with $\ge55\%$ fewer parameters than deep learning baselines, especially TabNet, while an $\eta$-centric MPL configuration also demonstrates improved accuracy with comparable efficiency. These results establish the promise of physics-guided GNNs for deployment in resource-constrained trigger systems.

Related papers

• FEM-Informed Hypergraph Neural Networks for Efficient Elastoplasticity [3.211924713637672]
  Graph neural networks (GNNs) naturally align with sparse operators and unstructured discretizations.<n>Motivated by discrete physics losses, we embed finite-element computations at nodes and Gauss points directly into message-passing layers.<n>We propose a numerically consistent FEM-Informed Hypergraph Neural Networks (FHGNN)
  arXiv  Detail & Related papers  (2026-02-07T05:11:12Z)
• Graph Network-based Structural Simulator: Graph Neural Networks for Structural Dynamics [40.190675168132124]
  We introduce the Graph Network-based Structural Simulator (GNSS), a GNN framework for surrogate modeling of dynamic structural problems.<n>We evaluate on a case study involving a beam excited by a 50kHz Hanning-modulated pulse.<n>The results show that accurately reproduces the physics of the problem over hundreds of timesteps and generalizes to unseen loading conditions, where existing GNNs fail to converge or deliver meaningful predictions.
  arXiv  Detail & Related papers  (2025-10-29T16:47:24Z)
• Toward accurate RUL and SOH estimation using reinforced graph-based PINNs enhanced with dynamic weights [0.0]
  We propose a framework that combines physics-based supervision with advanced-temporal learning.<n>Q-learning agents dynamically assign weights to physics-informed loss terms, improving generalization across real-time industrial systems.<n>In both RUL and SOH estimation tasks, the proposed method consistently outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2025-07-13T19:49:12Z)
• Scalable Graph Generative Modeling via Substructure Sequences [50.32639806800683]
  We introduce Generative Graph Pattern Machine (G$2$PM), a generative Transformer pre-training framework for graphs.<n>G$2$PM represents graph instances (nodes, edges, or entire graphs) as sequences of substructures.<n>It employs generative pre-training over the sequences to learn generalizable and transferable representations.
  arXiv  Detail & Related papers  (2025-05-22T02:16:34Z)
• Decentralized Nonconvex Composite Federated Learning with Gradient Tracking and Momentum [78.27945336558987]
  Decentralized server (DFL) eliminates reliance on client-client architecture.<n>Non-smooth regularization is often incorporated into machine learning tasks.<n>We propose a novel novel DNCFL algorithm to solve these problems.
  arXiv  Detail & Related papers  (2025-04-17T08:32:25Z)
• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Positional Encoder Graph Quantile Neural Networks for Geographic Data [4.277516034244117]
  We propose a novel framework that combines PE-GNNs with Quantile Neural Networks, partially monotonic neural blocks, and post-hoc recalibration techniques.<n>The PE-GQNN enables flexible and robust conditional density estimation with minimal assumptions about the target distribution, and it extends naturally to tasks beyond spatial data.
  arXiv  Detail & Related papers  (2024-09-27T16:02:12Z)
• Spatiotemporal Graph Learning with Direct Volumetric Information Passing and Feature Enhancement [62.91536661584656]
  We propose a dual-module framework, Cell-embedded and Feature-enhanced Graph Neural Network (aka, CeFeGNN) for learning.<n>We embed learnable cell attributions to the common node-edge message passing process, which better captures the spatial dependency of regional features.<n>Experiments on various PDE systems and one real-world dataset demonstrate that CeFeGNN achieves superior performance compared with other baselines.
  arXiv  Detail & Related papers  (2024-09-26T16:22:08Z)
• DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
  Graph neural networks are recognized for their strong performance across various applications. BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
  arXiv  Detail & Related papers  (2024-06-04T07:24:51Z)
• Quantifying the Optimization and Generalization Advantages of Graph Neural Networks Over Multilayer Perceptrons [50.33260238739837]
  Graph networks (GNNs) have demonstrated remarkable capabilities in learning from graph-structured data.<n>There remains a lack of analysis comparing GNNs and generalizations from an optimization and generalization perspective.
  arXiv  Detail & Related papers  (2023-06-24T10:21:11Z)
• Equivariant Graph Neural Networks for Charged Particle Tracking [1.6626046865692057]
  EuclidNet is a novel symmetry-equivariant GNN for charged particle tracking. We benchmark it against the state-of-the-art Interaction Network on the TrackML dataset. Our results show that EuclidNet achieves near-state-of-the-art performance at small model scales.
  arXiv  Detail & Related papers  (2023-04-11T15:43:32Z)
• GNN at the Edge: Cost-Efficient Graph Neural Network Processing over Distributed Edge Servers [24.109721494781592]
  Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions. This paper studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We show that our approach achieves superior performance over de facto baselines with more than 95.8% cost eduction in a fast convergence speed.
  arXiv  Detail & Related papers  (2022-10-31T13:03:16Z)
• Space-Time Graph Neural Networks with Stochastic Graph Perturbations [100.31591011966603]
  Space-time graph neural networks (ST-GNNs) learn efficient graph representations of time-varying data. In this paper we revisit the properties of ST-GNNs and prove that they are stable to graph stabilitys. Our analysis suggests that ST-GNNs are suitable for transfer learning on time-varying graphs.
  arXiv  Detail & Related papers  (2022-10-28T16:59:51Z)
• Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks [52.566735716983956]
  We propose a graph gradual pruning framework termed CGP to dynamically prune GNNs. Unlike LTH-based methods, the proposed CGP approach requires no re-training, which significantly reduces the computation costs. Our proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of existing methods.
  arXiv  Detail & Related papers  (2022-07-18T14:23:31Z)
• Edge Graph Neural Networks for Massive MIMO Detection [15.970981766599035]
  Massive Multiple-Input Multiple-Out (MIMO) detection is an important problem in modern wireless communication systems. While traditional Belief Propagation (BP) detectors perform poorly on loopy graphs, the recent Graph Neural Networks (GNNs)-based method can overcome the drawbacks of BP and achieve superior performance.
  arXiv  Detail & Related papers  (2022-05-22T08:01:47Z)
• 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)

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.