GraphNeT

GraphNeT: Graph neural networks for neutrino telescope event reconstruction - Published in JOSS (2023)

https://github.com/graphnet-team/graphnet

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 10 DOI reference(s) in README and JOSS metadata
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Academic publication links
Links to: arxiv.org, joss.theoj.org, zenodo.org
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Committers with academic emails
12 of 45 committers (26.7%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
Published in Journal of Open Source Software

Keywords

astrophysics deep-learning gpu graph-neural-network high-energy-physics machine-learning neural-network neutrino-oscillations neutrino-physics neutrinos particle-physics physics-analysis python pytorch

Scientific Fields

Engineering Computer Science - 40% confidence

Last synced: 4 months ago · JSON representation

Repository

A Deep learning library for neutrino telescopes

Basic Info

Host: GitHub
Owner: graphnet-team
License: apache-2.0
Language: Python
Default Branch: main
Homepage: https://graphnet-team.github.io/graphnet/
Size: 255 MB

Statistics

Stars: 103
Watchers: 5
Forks: 106
Open Issues: 64
Releases: 8

Topics

Created over 4 years ago · Last pushed 4 months ago

Metadata Files

Readme License Citation

README.md

![logo](./assets/identity/graphnet-logo-and-wordmark.png) | Usage | Development | |--------------------------------------------------------------------------------------------------------------------------------------------------------------------| --- | | [![status](https://joss.theoj.org/papers/eecab02fb1ecd174a5273750c1ea0baf/status.svg)](https://joss.theoj.org/papers/eecab02fb1ecd174a5273750c1ea0baf) | ![build](https://github.com/graphnet-team/graphnet/actions/workflows/build.yml/badge.svg) | | [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.6720188.svg)](https://doi.org/10.5281/zenodo.6720188) | ![code-quality](https://github.com/graphnet-team/graphnet/actions/workflows/code-quality.yml/badge.svg) | | [![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0) | [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) | | ![Supported python versions](https://img.shields.io/badge/python-%20%7C%203.9%20%7C%203.10%20%7C%203.11-blue) | [![Maintainability](https://api.codeclimate.com/v1/badges/b273a774112e32643162/maintainability)](https://codeclimate.com/github/graphnet-team/graphnet/maintainability) | | [![Docker image](https://img.shields.io/docker/v/asogaard/graphnet?color=blue&logo=docker&sort=semver)](https://hub.docker.com/repository/docker/asogaard/graphnet) | [![Test Coverage](https://api.codeclimate.com/v1/badges/b273a774112e32643162/test_coverage)](https://codeclimate.com/github/graphnet-team/graphnet/test_coverage) |

:rocket: About

GraphNeT is an open-source Python framework aimed at providing high quality, user friendly, end-to-end functionality to perform reconstruction tasks at neutrino telescopes using deep learning (DL). GraphNeT makes it fast and easy to train complex models that can provide event reconstruction with state-of-the-art performance, for arbitrary detector configurations, with inference times that are orders of magnitude faster than traditional reconstruction techniques.

Feel free to join the GraphNeT Slack group!

Publications using GraphNeT

| Type | Title | DOI | | --- | --- | --- | | Proceeding | Extending the IceCube search for neutrino point sources in the Northern sky with additional years of data | | | Proceeding | Sensitivity of the IceCube Upgrade to Atmospheric Neutrino Oscillations | | | Paper | GraphNeT: Graph neural networks for neutrino telescope event reconstruction | | | Paper | Graph Neural Networks for low-energy event classification & reconstruction in IceCube | |

:gear: Install

GraphNeT is compatible with Python 3.9 - 3.11, Linux and macOS, and we recommend installing graphnet in a separate virtual environment. To install GraphNeT, please follow the installation instructions

:ringed_planet: Use cases

Below is an incomplete list of potential use cases for Deep Learning in neutrino telescopes. These are categorised as either "Reconstruction challenges" that are considered common and that may benefit several experiments physics analyses; and those same "Experiments" and "Physics analyses".

Reconstruction challenges

| Title | Status | People | Materials | | --- | --- | --- | --- | | Low-energy neutrino classification and reconstruction | Done | Rasmus rse | https://arxiv.org/abs/2209.03042 | | High-energy neutrino classification and reconstruction | Active | Rasmus rse | | | Pulse noise cleaning | Paused | Rasmus rse, Kaare Iversen (past), Morten Holm | | | (In-)elasticity reconstruction | Paused | Marc Jacquart (past) | | | Multi-class event classification | Active | Morten Holm, Peter Andresen | | | Data/MC difference mitigation | | | | | Systematic uncertainty mitigation | | | |

Experiments

| Title | Status | People | Materials | | --- | --- | --- | --- | | IceCube | Active | (...) | | | IceCube-Upgrade | Active | (...) | | | IceCube-Gen2 | Active | (...) | | | P-ONE | | (...) | | | KM3NeT-ARCA | | (...) | | | KM3NeT-ORCA | | (...) | |

Physics analyses

| Title | Status | People | Materials | | --- | --- | --- | --- | | Neutrino oscillations | || | | Point source searches | || | | Low-energy cosmic alerts | || | | High-energy cosmic alerts | || | | Moon pointing | || | | Muon decay asymmetry | || | | Spectra measurements | || |

:handshake: Contributing

To make sure that the process of contributing is as smooth and effective as possible, we provide a few guidelines in the contributing guide that we encourage contributors to follow.

In short, everyone who wants to contribute to this project is more than welcome to do so! Contributions are handled through pull requests, that should be linked to a GitHub issue describing the feature to be added or bug to be fixed. Pull requests will be reviewed by the project maintainers and merged into the main branch when accepted.

:memo: License

GraphNeT has an Apache 2.0 license, as found in the LICENSE file.

:raised_hands: Acknowledgements

This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Skodowska-Curie grant agreement No. 890778, and the PUNCH4NFDI consortium via DFG fund NFDI39/1.

[^1]: Examples of this are shown in the examples/01icetray/01converti3files.py script

Owner

Name: GraphNeT
Login: graphnet-team
Kind: organization

Repositories: 2
Profile: https://github.com/graphnet-team

Graph neural networks for neutrino telescope event reconstruction

JOSS Publication

GraphNeT: Graph neural networks for neutrino telescope event reconstruction

Published

May 12, 2023

DOI

10.21105/joss.04971

Volume 8, Issue 85, Page 4971