Search Results for author: Gage DeZoort

Found 8 papers, 4 papers with code

High Pileup Particle Tracking with Object Condensation

1 code implementation6 Dec 2023 Kilian Lieret, Gage DeZoort, Devdoot Chatterjee, Jian Park, Siqi Miao, Pan Li

Recent work has demonstrated that graph neural networks (GNNs) can match the performance of traditional algorithms for charged particle tracking while improving scalability to meet the computing challenges posed by the HL-LHC.

Edge Classification Object

Principles for Initialization and Architecture Selection in Graph Neural Networks with ReLU Activations

no code implementations20 Jun 2023 Gage DeZoort, Boris Hanin

We then prove that using residual aggregation operators, obtained by interpolating a fixed aggregation operator with the identity, provably alleviates oversmoothing at initialization.

Graph Neural Networks in Particle Physics: Implementations, Innovations, and Challenges

no code implementations23 Mar 2022 Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S. Neubauer, Kazuhiro Terao

Where previously these sets of data have been formulated as series or image data to match the available machine learning architectures, with the advent of graph neural networks (GNNs), these systems can be learned natively as graphs.

Graph Neural Networks for Charged Particle Tracking on FPGAs

no code implementations3 Dec 2021 Abdelrahman Elabd, Vesal Razavimaleki, Shi-Yu Huang, Javier Duarte, Markus Atkinson, Gage DeZoort, Peter Elmer, Scott Hauck, Jin-Xuan Hu, Shih-Chieh Hsu, Bo-Cheng Lai, Mark Neubauer, Isobel Ojalvo, Savannah Thais, Matthew Trahms

The determination of charged particle trajectories in collisions at the CERN Large Hadron Collider (LHC) is an important but challenging problem, especially in the high interaction density conditions expected during the future high-luminosity phase of the LHC (HL-LHC).


Charged particle tracking via edge-classifying interaction networks

1 code implementation30 Mar 2021 Gage DeZoort, Savannah Thais, Javier Duarte, Vesal Razavimaleki, Markus Atkinson, Isobel Ojalvo, Mark Neubauer, Peter Elmer

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 high energy particle physics.

Edge Classification graph construction

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