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arxiv: 2606.02788 · v1 · pith:J2SRPUQVnew · submitted 2026-06-01 · 🌌 astro-ph.IM · cs.LG

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

Floriano Tori , Brecht Verbeken , Vincent Ginis This is my paper

Pith reviewed 2026-06-28 12:21 UTC · model grok-4.3

classification 🌌 astro-ph.IM cs.LG
keywords IceCubeneutrino direction reconstructionconvolutional neural networksimage encodingfingerprintsResNetastrophysics instrumentation
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The pith

Neutrino fingerprints as 72x72x3 images let ResNet18 reconstruct IceCube directions with 1.10 rad mean angular error.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The authors propose representing IceCube neutrino detection events as compact images called neutrino fingerprints, sized 72 by 72 pixels with three color channels that encode the timing and charge of pulses at each detector. This transforms the original sparse and irregular pulse data into a format that standard convolutional neural networks can process efficiently. They apply a ResNet18 model to these fingerprints from the public Kaggle dataset of simulated events and report a mean angular error of 1.10 radians for direction reconstruction. A reader would care because this method provides an interpretable baseline that performs comparably to more elaborate techniques, potentially simplifying the analysis of high-energy neutrino events from astrophysical sources.

Core claim

By encoding the sparse pulse data from IceCube detectors into dense 72x72x3 neutrino fingerprint images, where each pixel represents a detector and the color channels capture timing and charge statistics, a ResNet18 convolutional network can achieve a mean angular error of 1.10 rad in reconstructing the direction of incoming neutrinos, offering an effective and interpretable baseline that rivals more complex architectures.

What carries the argument

Neutrino fingerprints, defined as 72x72x3 images that map each IceCube detector to a pixel and encode pulse timing and charge as color channels, enabling direct application of convolutional neural networks to the direction reconstruction task.

If this is right

  • Standard CNN architectures like ResNet18 can be trained directly on the encoded images to reconstruct neutrino directions.
  • The achieved mean angular error of 1.10 rad demonstrates competitiveness with more complex reconstruction methods.
  • This image-based approach provides a more interpretable alternative for IceCube event analysis.
  • The fingerprint encoding retains sufficient directional information from the simulated events for accurate reconstruction.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The method could be adapted to other neutrino or particle detectors with similar sparse readout systems.
  • Real-time direction reconstruction might become more accessible if the image encoding simplifies the pipeline.
  • Future work could explore combining this with other machine learning techniques to further reduce the angular error.

Load-bearing premise

The Kaggle simulated events accurately capture the statistical properties of real IceCube data, and the 72x72x3 fingerprint encoding preserves all directional information from the original sparse pulses.

What would settle it

Demonstrating that the ResNet18 model trained on fingerprints performs substantially worse on actual IceCube observational data than on the simulated Kaggle events would falsify the claim that this is an effective baseline.

Figures

Figures reproduced from arXiv: 2606.02788 by Brecht Verbeken, Floriano Tori, Vincent Ginis.

Figure 1
Figure 1. Figure 1: We show that 2D images of neutrino events, using a convolution based architectures, can be used to reconstruct the directionality of an event. (a.) A cascade type event transformed using our fingerprint method. (b.) Standard event transformed (c.) Test Mean Angular Error evolution during training for different losses averaged over three seeds. 4 Results, Framing, and Outlook Our single, compact CNN—trained… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The manuscript introduces 'neutrino fingerprints' as compact 72×72×3 images encoding IceCube events, with each pixel corresponding to a DOM and pulse timing/charge statistics mapped to color channels. It reports that a ResNet18 CNN trained on these images from the Kaggle Neutrinos in Deep Ice competition data achieves a mean angular error of 1.10 rad, positioning the approach as an effective, interpretable baseline that rivals more complex architectures for direction reconstruction.

Significance. If the performance holds under proper validation and the encoding is shown to preserve directional information, the work could supply a straightforward image-based method for IceCube event reconstruction that leverages standard CNNs and public benchmark data. The idea of transforming sparse pulses into dense images is conceptually straightforward and could aid interpretability, but the current lack of supporting details prevents a full assessment of its contribution to the field.

major comments (3)
  1. [Abstract] Abstract: The headline result of 1.10 rad mean angular error is stated as a single number with no accompanying information on training procedure, data splits (train/validation/test), optimization hyperparameters, baseline comparisons, statistical uncertainties, or ablation studies, rendering the support for the central performance claim unevaluable.
  2. [Abstract] Abstract: The 72×72×3 fingerprint construction is described only at a high level; the precise 3D-to-2D mapping of the ~5160 DOMs onto the grid (including assignment of string and depth coordinates) is unspecified, which directly affects whether relative geometry and timing information required for direction reconstruction is retained.
  3. [Abstract] Abstract: No quantitative comparison is provided to either raw-pulse baselines or alternative architectures, so it is impossible to determine whether the fingerprint + ResNet18 combination actually rivals more complex methods or simply reproduces expected performance on the Kaggle simulations.
minor comments (1)
  1. [Abstract] Abstract: The color-channel encoding (timing and charge statistics) is mentioned but not defined precisely enough to allow reproduction or to assess information loss.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We address each major comment below and will revise the abstract and relevant sections to improve clarity and support for our claims.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The headline result of 1.10 rad mean angular error is stated as a single number with no accompanying information on training procedure, data splits (train/validation/test), optimization hyperparameters, baseline comparisons, statistical uncertainties, or ablation studies, rendering the support for the central performance claim unevaluable.

    Authors: We agree the abstract would benefit from additional context. In the revision we will expand it to note the use of the Kaggle train/test split, standard ResNet18 training with cross-entropy loss on angular error, and refer readers to the methods section for hyperparameters, uncertainties, and ablation details. revision: yes

  2. Referee: [Abstract] Abstract: The 72×72×3 fingerprint construction is described only at a high level; the precise 3D-to-2D mapping of the ~5160 DOMs onto the grid (including assignment of string and depth coordinates) is unspecified, which directly affects whether relative geometry and timing information required for direction reconstruction is retained.

    Authors: The full manuscript details the mapping in Section 3, where DOMs are projected onto a 72×72 grid ordered by string number and depth to preserve relative geometry. We will add a concise description of this procedure to the abstract in the revised version. revision: yes

  3. Referee: [Abstract] Abstract: No quantitative comparison is provided to either raw-pulse baselines or alternative architectures, so it is impossible to determine whether the fingerprint + ResNet18 combination actually rivals more complex methods or simply reproduces expected performance on the Kaggle simulations.

    Authors: The work is framed as an interpretable baseline rather than a state-of-the-art claim. We will add a brief comparison table in the results section against a simple raw-pulse MLP baseline and reference Kaggle leaderboard performance of more complex entries to better contextualize the 1.10 rad result. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper reports an empirical performance result (mean angular error of 1.10 rad) measured directly on held-out Kaggle competition simulation data after training a standard ResNet18 on the proposed 72x72x3 fingerprint encoding. No equations, fitted parameters, or predictions are defined in terms of the target metric itself, and the provided text contains no self-citations or uniqueness claims that reduce the central result to prior author work. The derivation chain consists of a data transformation followed by standard supervised training and evaluation, which remains self-contained against the external benchmark.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the validity of the Kaggle simulation and the information-preserving property of the image encoding. No free parameters or invented entities are introduced in the abstract.

axioms (2)
  • domain assumption The Kaggle competition simulated events are statistically representative of real IceCube observations.
    Performance is reported exclusively on the public simulated dataset.
  • domain assumption The 72x72x3 fingerprint representation retains sufficient directional information from the original sparse pulse data.
    The method assumes the color-channel encoding does not discard critical timing or geometry cues.

pith-pipeline@v0.9.1-grok · 5660 in / 1319 out tokens · 24984 ms · 2026-06-28T12:21:32.304888+00:00 · methodology

discussion (0)

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