GraphNeT: Graph neural networks for neutrino telescope event reconstruction

Python Submitted 06 October 2022Published 12 May 2023
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Authors

Andreas Søgaard (0000-0002-0823-056X), Rasmus F. Ørsøe (0000-0001-8890-4124), Morten Holm (0000-0003-1383-2810), Leon Bozianu (0000-0002-1243-9980), Aske Rosted (0000-0003-2410-400X), Troels C. Petersen (0000-0003-0221-3037), Kaare Endrup Iversen (0000-0001-6533-4085), Andreas Hermansen (0009-0006-1162-9770), Tim Guggenmos, Peter Andresen (0009-0008-5759-0490), Martin Ha Minh (0000-0001-7776-4875), Ludwig Neste (0000-0002-4829-3469), Moust Holmes (0009-0000-8530-7041), Axel Pontén (0009-0008-2463-2930), Kayla Leonard DeHolton (0000-0002-8795-0601), Philipp Eller (0000-0001-6354-5209)

Citation

Søgaard et al., (2023). GraphNeT: Graph neural networks for neutrino telescope event reconstruction. Journal of Open Source Software, 8(85), 4971, https://doi.org/10.21105/joss.04971

@article{Søgaard2023, doi = {10.21105/joss.04971}, url = {https://doi.org/10.21105/joss.04971}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {85}, pages = {4971}, author = {Søgaard, Andreas and Ørsøe, Rasmus F. and Holm, Morten and Bozianu, Leon and Rosted, Aske and Petersen, Troels C. and Iversen, Kaare Endrup and Hermansen, Andreas and Guggenmos, Tim and Andresen, Peter and Minh, Martin Ha and Neste, Ludwig and Holmes, Moust and Pontén, Axel and DeHolton, Kayla Leonard and Eller, Philipp}, title = {GraphNeT: Graph neural networks for neutrino telescope event reconstruction}, journal = {Journal of Open Source Software} }
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machine learning deep learning neural networks graph neural networks astrophysics particle physics neutrinos

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ISSN 2475-9066