{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/graph-neural-networks-for-icecube-signal","title":"Graph Neural Networks for IceCube Signal Classification","arxiv_id":"1809.06166","date":"2018-09-17","proceeding":null,"authors":["Nicholas Choma","Federico Monti","Lisa Gerhardt","Tomasz Palczewski","Zahra Ronaghi","Prabhat","Wahid Bhimji","Michael M. Bronstein","Spencer R. Klein","Joan Bruna"],"abstract":"Tasks involving the analysis of geometric (graph- and manifold-structured)\ndata have recently gained prominence in the machine learning community, giving\nbirth to a rapidly developing field of geometric deep learning. In this work,\nwe leverage graph neural networks to improve signal detection in the IceCube\nneutrino observatory. The IceCube detector array is modeled as a graph, where\nvertices are sensors and edges are a learned function of the sensors' spatial\ncoordinates. As only a subset of IceCube's sensors is active during a given\nobservation, we note the adaptive nature of our GNN, wherein computation is\nrestricted to the input signal support. We demonstrate the effectiveness of our\nGNN architecture on a task classifying IceCube events, where it outperforms\nboth a traditional physics-based method as well as classical 3D convolution\nneural networks.","url_abs":"http://arxiv.org/abs/1809.06166v1","url_pdf":"http://arxiv.org/pdf/1809.06166v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"graph-neural-networks-for-icecube-signal","repo_url":"https://github.com/WIPACrepo/NuIntClassification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.06166","atlas_url":"https://app.syntology.ai/?focus=1809.06166","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}