将中微子事件转为图像，用卷积网络精准预测方向。
Neutrino Fingerprints: Image-Based Encodings of IceCube Events for CNN Direction Reconstruction
- 把探测器脉冲数据转成72×72彩色图像，每像素对应一个探测器。
- 用ResNet18模型实现1.10弧度的平均角度误差，性能媲美复杂模型。
- 适合做中微子方向重建的可解释基线，也适用于其他稀疏数据处理。
在冰立方中微子观测站中重构入射中微子的方向是天体物理学中的重要问题。公开的IceCube--深冰中微子Kaggle竞赛提供了1.4亿个模拟事件用于基准测试重建技术。为从新视角解决此问题，我们提出中微子指纹：一种紧凑的72×72×3图像表示，其中每个像素代表一个探测器，脉冲时间与电荷统计信息编码为颜色通道。该表示将稀疏、不规则的脉冲数据转化为适合卷积处理的密集图像。我们的ResNet18模型实现1.10弧度的平均角度误差，表明基于指纹训练的卷积网络可媲美更复杂的架构，同时提供了一种高效且可解释的冰立方事件重建基线。
原文摘要 · Abstract (English)
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.
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