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Found 9 papers, 4 papers with code
Novel deep learning methods for track reconstruction
The second set of models use Graph Neural Networks (GNNs) for the tasks of hit classification and segment classification.
High Energy Physics - Experiment Data Analysis, Statistics and Probability
Performance of a Geometric Deep Learning Pipeline for HL-LHC Particle Tracking
The Exa. TrkX project has applied geometric learning concepts such as metric learning and graph neural networks to HEP particle tracking.
A Dynamic Reduction Network for Point Clouds
Classifying whole images is a classic problem in machine learning, and graph neural networks are a powerful methodology to learn highly irregular geometries.
Accelerated Charged Particle Tracking with Graph Neural Networks on FPGAs
We develop and study FPGA implementations of algorithms for charged particle tracking based on graph neural networks.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
Track Seeding and Labelling with Embedded-space Graph Neural Networks
Detector information can be associated with nodes and edges, enabling a GNN to propagate the embedded parameters around the graph and predict node-, edge- and graph-level observables.
Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision.
Instrumentation and Detectors High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability
Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC).
Object Reconstruction
High Energy Physics - Experiment
Data Science and Machine Learning in Education
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research.
