Search Results for author: Daniel Murnane

Found 9 papers, 2 papers with code

Equivariant Graph Neural Networks for Charged Particle Tracking

1 code implementation11 Apr 2023 Daniel Murnane, Savannah Thais, Ameya Thete

Graph neural networks (GNNs) have gained traction in high-energy physics (HEP) for their potential to improve accuracy and scalability.

Benchmarking GPU and TPU Performance with Graph Neural Networks

no code implementations21 Oct 2022 Xiangyang Ju, Yunsong Wang, Daniel Murnane, Nicholas Choma, Steven Farrell, Paolo Calafiura

Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models.


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.

Symmetry Group Equivariant Architectures for Physics

no code implementations11 Mar 2022 Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, David W. Miller, Daniel Murnane, Jan T. Offermann, Mariel Pettee, Phiala Shanahan, Chase Shimmin, Savannah Thais

Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe.

BIG-bench Machine Learning

Semi-Equivariant GNN Architectures for Jet Tagging

no code implementations14 Feb 2022 Daniel Murnane, Savannah Thais, Jason Wong

Composing Graph Neural Networks (GNNs) of operations that respect physical symmetries has been suggested to give better model performance with a smaller number of learnable parameters.

Jet Tagging

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

no code implementations25 Mar 2020 Xiangyang Ju, Steven Farrell, Paolo Calafiura, Daniel Murnane, Prabhat, Lindsey Gray, Thomas Klijnsma, Kevin Pedro, Giuseppe Cerati, Jim Kowalkowski, Gabriel Perdue, Panagiotis Spentzouris, Nhan Tran, Jean-Roch Vlimant, Alexander Zlokapa, Joosep Pata, Maria Spiropulu, Sitong An, Adam Aurisano, Jeremy Hewes, Aristeidis Tsaris, Kazuhiro Terao, Tracy Usher

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

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