Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction
arXiv:2606.02788v1 Announce Type: cross 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, ir
The paper leverages a recent Kaggle competition and advances in CNNs to propose a novel image-based encoding method for neutrino reconstruction, indicating continued innovation in astrophysics analysis techniques.
This development could significantly improve the accuracy and efficiency of neutrino direction reconstruction, enhancing our ability to study cosmic phenomena and fundamental physics through neutrino observatories.
A new method for processing sparse detector data into a dense, image-like format for deep learning is introduced, potentially setting a new standard for event reconstruction in physics experiments.
- · Astrophysicists
- · Machine learning researchers
- · Neutrino observatories
- · Computational science
- · Traditional statistical reconstruction methods
- · Less efficient data processing techniques
Improved precision in identifying neutrino sources and understanding their origins.
Accelerated discovery of new astrophysical phenomena reliant on accurate neutrino data.
Potential for this image-based encoding method to be adapted and applied to other high-energy physics experiments with sparse detector arrays.
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