Výsledky vyhledávání - "Conlon, Sean"

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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. Exa.TrkX's tracking pipeline groups detector measurements to form track candidates and filters them. The pipeline

Externí odkaz: http://arxiv.org/abs/2103.06995

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Graph Neural Network for Object Reconstruction in Liquid Argon Time Projection Chambers

Autor: Hewes, V, Aurisano, Adam, Cerati, Giuseppe, Kowalkowski, Jim, Lee, Claire, Liao, Wei-keng, Day, Alexandra, Agrawal, Ankit, Spiropulu, Maria, Vlimant, Jean-Roch, Gray, Lindsey, Klijnsma, Thomas, Calafiura, Paolo, Conlon, Sean, Farrell, Steve, Ju, Xiangyang, Murnane, Daniel

This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC). GNNs are still a relatively novel technique, and have shown great promise for similar

Externí odkaz: http://arxiv.org/abs/2103.06233

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Track Seeding and Labelling with Embedded-space Graph Neural Networks

Autor: Choma, Nicholas, Murnane, Daniel, Ju, Xiangyang, Calafiura, Paolo, Conlon, Sean, Farrell, Steven, Prabhat, Cerati, Giuseppe, Gray, Lindsey, Klijnsma, Thomas, Kowalkowski, Jim, Spentzouris, Panagiotis, Vlimant, Jean-Roch, Spiropulu, Maria, Aurisano, Adam, Hewes, V, Tsaris, Aristeidis, Terao, Kazuhiro, Usher, Tracy

To address the unprecedented scale of HL-LHC data, the Exa.TrkX project is investigating a variety of machine learning approaches to particle track reconstruction. The most promising of these solutions, graph neural networks (GNN), process the event

Externí odkaz: http://arxiv.org/abs/2007.00149

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