Daniel Murnane

According to our database, Daniel Murnane authored at least 12 papers between 2020 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

ColliderML: The First Release of an OpenDataDetector High-Luminosity Physics Benchmark Dataset.

Doga Elitez, Paul Gessinger, Daniel Murnane, Marcus Selchou Raaholt, Andreas Salzburger, Stine Kofoed Skov, Andreas Stefl, Anna Zaborowska

CoRR, December, 2025

2024

A Language Model for Particle Tracking.

Andris Huang, Yash Melkani, Paolo Calafiura, Alina Lazar, Daniel Thomas Murnane, Minh-Tuan Pham, Xiangyang Ju

CoRR, 2024

2023

Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks.

Savannah Thais, Daniel Murnane

CoRR, 2023

Graph Structure from Point Clouds: Geometric Attention is All You Need.

Daniel Murnane

CoRR, 2023

Equivariant Graph Neural Networks for Charged Particle Tracking.

Daniel Murnane, Savannah Thais, Ameya Thete

CoRR, 2023

Hierarchical Graph Neural Networks for Particle Track Reconstruction.

Ryan Liu, Paolo Calafiura, Steven Farrell, Xiangyang Ju, Daniel Thomas Murnane, Tuan Minh Pham

CoRR, 2023

2022

Benchmarking GPU and TPU Performance with Graph Neural Networks.

Xiangyang Ju, Yunsong Wang, Daniel Murnane, Nicholas Choma, Steven Farrell, Paolo Calafiura

CoRR, 2022

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

Savannah Thais, Paolo Calafiura, Grigorios Chachamis, Gage DeZoort, Javier M. Duarte, Sanmay Ganguly, Michael Kagan, Daniel Murnane, Mark S. Neubauer, Kazuhiro Terao

CoRR, 2022

Symmetry Group Equivariant Architectures for Physics.

Alexander Bogatskiy, Sanmay Ganguly, Thomas Kipf, Risi Kondor, David W. Miller, Daniel Murnane, Jan T. Offermann, Mariel Pettee, Phiala E. Shanahan, Chase Shimmin, Savannah Thais

CoRR, 2022

Semi-Equivariant GNN Architectures for Jet Tagging.

Daniel Murnane, Savannah Thais, Jason Wong

CoRR, 2022

2021

Physics and Computing Performance of the Exa.TrkX TrackML Pipeline.

Xiangyang Ju, Daniel Murnane, Paolo Calafiura, Nicholas Choma, Sean Conlon, Steven Farrell, Yaoyuan Xu, Maria Spiropulu, Jean-Roch Vlimant, Adam Aurisano, Jeremy Hewes, Giuseppe Cerati, Lindsey Gray, Thomas Klijnsma, Jim Kowalkowski, Markus Atkinson, Mark S. Neubauer, Gage DeZoort, Savannah Thais, Aditi Chauhan, Alex Schuy, Shih-Chieh Hsu, Alexandra Ballow, Alina Lazar

CoRR, 2021

2020

Track Seeding and Labelling with Embedded-space Graph Neural Networks.

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

CoRR, 2020