Search Results for author:
Found 11 papers, 4 papers with code
A Language Model for Particle Tracking
In this paper, we present a tokenized detector representation that allows us to train a BERT model for particle tracking.
Hierarchical Graph Neural Networks for Particle Track Reconstruction
We introduce a novel variant of GNN for particle tracking called Hierarchical Graph Neural Network (HGNN).
Benchmarking GPU and TPU Performance with Graph Neural Networks
Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models.
Hyperparameter Optimization of Generative Adversarial Network Models for High-Energy Physics Simulations
We show that given proper hyperparameter tuning, we can find GANs that provide high-quality approximations of the desired quantities.
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.
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
Beyond 4D Tracking: Using Cluster Shapes for Track Seeding
The shape of clusters provides an additional dimension for track seeding that can significantly reduce the combinatorial challenge of track finding.
Supervised Jet Clustering with Graph Neural Networks for Lorentz Boosted Bosons
By labeling simulated final state hadrons as descending from an uncolored particle, it is possible to train a supervised learning method to create boson jets.
High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability
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
Dark Matter Benchmark Models for Early LHC Run-2 Searches: Report of the ATLAS/CMS Dark Matter Forum
This document is the final report of the ATLAS-CMS Dark Matter Forum, a forum organized by the ATLAS and CMS collaborations with the participation of experts on theories of Dark Matter, to select a minimal basis set of dark matter simplified models that should support the design of the early LHC Run-2 searches.
High Energy Physics - Experiment High Energy Physics - Phenomenology
