{"ID":3006041,"CreatedAt":"2026-06-03T03:09:48.883664427Z","UpdatedAt":"2026-06-04T18:42:04.470931947Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.02788","arxiv_id":"2606.02788","title":"Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction","abstract":"Reconstructing the direction of incoming neutrinos in the IceCube Neutrino Observatory is an important problem in astrophysics. The public IceCube--Neutrinos in Deep Ice Kaggle competition provided 140 million simulated events to benchmark reconstruction techniques. To address this challenge from a novel perspective we introduce neutrino fingerprints compact $72 \\times 72 \\times 3$ images in which each pixel represents a single detector, with pulse timing and charge statistics encoded as color channels. This representation transforms sparse, irregular pulse data into dense images suitable for convolutional processing. Our ResNet18 model achieves a mean angular error of $1.10$ rad, indicating that convolutional networks trained on fingerprints rival more complex architectures while offering an effective, interpretable baseline for IceCube event reconstruction.","short_abstract":"Reconstructing the direction of incoming neutrinos in the IceCube Neutrino Observatory is an important problem in astrophysics. The public IceCube--Neutrinos in Deep Ice Kaggle competition provided 140 million simulated events to benchmark reconstruction techniques. To address this challenge from a novel perspective we...","url_abs":"https://arxiv.org/abs/2606.02788","url_pdf":"https://arxiv.org/pdf/2606.02788v1","authors":"[\"Floriano Tori\",\"Brecht Verbeken\",\"Vincent Ginis\"]","published":"2026-06-01T18:54:43Z","proceeding":"astro-ph.IM","tasks":"[\"astro-ph.IM\",\"cs.LG\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}