Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction

• URL: http://arxiv.org/abs/2606.02788v1
• Date: Mon, 01 Jun 2026 18:54:43 GMT
• Title: Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction
• Abstract summary: IceCube--Neutrinos in Deep Ice Kaggle competition provided 140 million simulated events to benchmark reconstruction techniques.<n>We introduce neutrino fingerprints compact $72 times 72 times 3$ images in which each pixel represents a single detector.<n>This representation transforms sparse, irregular pulse data into dense images suitable for convolutional processing.
• Score: 5.3541640305805425
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Differentiable Neural Surrogate for Photon Propagation in Neutrino Telescopes [1.411614392022118]
  Large-volume neutrino telescopes infer neutrino properties from Cherenkov light.<n>We introduce candela, a differentiable SIREN neural field that learns the photon Green's function of the IceCube Neutrino Observatory.
  arXiv  Detail & Related papers  (2026-09-04T03:54:28Z)
• NuBench: An Open Benchmark for Deep Learning-Based Event Reconstruction in Neutrino Telescopes [0.0]
  We present NuBench, an open benchmark for deep learning-based event reconstruction in neutrino telescopes.<n>NuBench comprises seven large-scale simulated datasets containing nearly 130 million charged- and neutral-current muon-neutrino interactions.
  arXiv  Detail & Related papers  (2025-11-17T08:08:01Z)
• Haar Nuclear Norms with Applications to Remote Sensing Imagery Restoration [53.68392692185276]
  This paper proposes a novel low-rank regularization term, named the Haar nuclear norm (HNN), for efficient and effective remote sensing image restoration.<n>It leverages the low-rank properties of wavelet coefficients derived from the 2-D frontal slice-wise Haar discrete wavelet transform.<n> Experimental evaluations conducted on hyperspectral image inpainting, multi-temporal image cloud removal, and hyperspectral image denoising have revealed the HNN's potential.
  arXiv  Detail & Related papers  (2024-07-11T13:46:47Z)
• IReNe: Instant Recoloring of Neural Radiance Fields [54.94866137102324]
  We introduce IReNe, enabling swift, near real-time color editing in NeRF.<n>We leverage a pre-trained NeRF model and a single training image with user-applied color edits.<n>This adjustment allows the model to generate new scene views, accurately representing the color changes from the training image.
  arXiv  Detail & Related papers  (2024-05-30T09:30:28Z)
• Design and simulation of a transmon qubit chip for Axion detection [103.69390312201169]
  Device based on superconducting qubits has been successfully applied in detecting few-GHz single photons via Quantum Non-Demolition measurement (QND) In this study, we present Qub-IT's status towards the realization of its first superconducting qubit device.
  arXiv  Detail & Related papers  (2023-10-08T17:11:42Z)
• 2D Convolutional Neural Network for Event Reconstruction in IceCube DeepCore [0.0]
  IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies. We present a new CNN model that exploits time and depth translational symmetry in IceCube DeepCore data.
  arXiv  Detail & Related papers  (2023-07-31T02:37:36Z)
• Recent neutrino oscillation result with the IceCube experiment [14.645468999921961]
  The IceCube South Pole Neutrino Observatory is a Cherenkov detector instrumented in a cubic kilometer of ice at the South Pole. IceCube's primary scientific goal is the detection of TeV neutrino emissions from astrophysical sources. Advances in physics sensitivity have recently been achieved by employing Convolutional Neural Networks to reconstruct neutrino interactions in the DeepCore detector.
  arXiv  Detail & Related papers  (2023-07-29T01:12:26Z)
• Streamlined Lensed Quasar Identification in Multiband Images via Ensemble Networks [34.82692226532414]
  Quasars experiencing strong lensing offer unique viewpoints on subjects related to cosmic expansion rate, dark matter, and quasar host galaxies. We have developed a novel approach by ensembling cutting-edge convolutional networks (CNNs) trained on realistic galaxy-quasar lens simulations. We retrieve approximately 60 million sources as parent samples and reduce this to 892,609 after employing a photometry preselection to discover quasars with Einstein radii of $theta_mathrmE5$ arcsec.
  arXiv  Detail & Related papers  (2023-07-03T15:09:10Z)
• Trigger-Level Event Reconstruction for Neutrino Telescopes Using Sparse Submanifold Convolutional Neural Networks [0.0]
  Convolutional neural networks (CNNs) have seen extensive applications in scientific data analysis, including in neutrino telescopes. We propose sparse submanifold convolutions (SSCNNs) as a solution to these issues. We show that the SSCNN event reconstruction performance is comparable to or better than traditional and machine learning algorithms.
  arXiv  Detail & Related papers  (2023-03-15T17:59:01Z)
• Interpretable Joint Event-Particle Reconstruction for Neutrino Physics at NOvA with Sparse CNNs and Transformers [124.29621071934693]
  We present a novel neural network architecture that combines the spatial learning enabled by convolutions with the contextual learning enabled by attention. TransformerCVN simultaneously classifies each event and reconstructs every individual particle's identity. This architecture enables us to perform several interpretability studies which provide insights into the network's predictions.
  arXiv  Detail & Related papers  (2023-03-10T20:36:23Z)
• Graph Neural Networks for Low-Energy Event Classification & Reconstruction in IceCube [0.02512334342040507]
  We introduce a Graph Neural Network (GNN) as the classification and reconstruction method for IceCube events. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy. The GNN is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.
  arXiv  Detail & Related papers  (2022-09-07T10:16:53Z)
• Self-Denoising Neural Networks for Few Shot Learning [66.38505903102373]
  We present a new training scheme that adds noise at multiple stages of an existing neural architecture while simultaneously learning to be robust to this added noise. This architecture, which we call a Self-Denoising Neural Network (SDNN), can be applied easily to most modern convolutional neural architectures.
  arXiv  Detail & Related papers  (2021-10-26T03:28:36Z)
• Reducing the Sim-to-Real Gap for Event Cameras [64.89183456212069]
  Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called 'events' with unparalleled low latency. Recent work has demonstrated impressive results using Convolutional Neural Networks (CNNs) for video reconstruction and optic flow with events. We present strategies for improving training data for event based CNNs that result in 20-40% boost in performance of existing video reconstruction networks.
  arXiv  Detail & Related papers  (2020-03-20T02:44:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.