GraphNeT: Graph neural networks for neutrino telescope event reconstruction

Python Submitted 06 October 2022Published 12 May 2023

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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)


Søgaard et al., (2023). GraphNeT: Graph neural networks for neutrino telescope event reconstruction. Journal of Open Source Software, 8(85), 4971,

@article{Søgaard2023, doi = {10.21105/joss.04971}, url = {}, year = {2023}, publisher = {The Open Journal}, volume = {8}, number = {85}, pages = {4971}, author = {Andreas Søgaard and Rasmus F. Ørsøe and Morten Holm and Leon Bozianu and Aske Rosted and Troels C. Petersen and Kaare Endrup Iversen and Andreas Hermansen and Tim Guggenmos and Peter Andresen and Martin Ha Minh and Ludwig Neste and Moust Holmes and Axel Pontén and Kayla Leonard DeHolton and Philipp Eller}, 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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