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arxiv: 2604.23055 · v1 · submitted 2026-04-24 · ✦ hep-ex

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Incorporating Inelasticity Reconstruction into Neutrino Mass Ordering Studies with IceCube

J.H. Peterson , M. Jacquart (for the IceCube Collaboration)
Authors on Pith no claims yet

Pith reviewed 2026-05-08 09:12 UTC · model grok-4.3

classification ✦ hep-ex
keywords inelasticityneutrino mass orderingIceCubeDeepCoregraph neural networkconvolutional neural networkatmospheric neutrinososcillation
0
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The pith

Reconstructing inelasticity adds a fourth observable to improve neutrino mass ordering sensitivity in IceCube analyses.

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

Earth's matter affects atmospheric neutrino oscillations differently for neutrinos and antineutrinos based on the mass ordering. The paper develops two inelasticity reconstruction algorithms, one with a graph neural network and one with an ensemble of two-dimensional convolutional neural networks, to exploit the distinct inelasticity distributions arising from opposite chirality. Inelasticity is incorporated as a fourth observable together with particle energy, direction, and flavor. New sensitivities to the neutrino mass ordering are calculated for the IceCube DeepCore detector and the upcoming Upgrade to quantify the improvement from this addition.

Core claim

Two inelasticity reconstruction methods are developed and their performance assessed. The reconstructed inelasticity is then treated as an additional observable to recompute the neutrino mass ordering sensitivities, determining the impact of this variable on measurements with IceCube DeepCore and the IceCube Upgrade.

What carries the argument

Inelasticity reconstruction algorithms (graph neural network and ensemble of two-dimensional convolutional neural networks) that supply the fraction of energy transferred to the nucleon as a statistically discriminating observable between neutrinos and antineutrinos.

If this is right

  • The neutrino mass ordering sensitivity for IceCube DeepCore improves when inelasticity is included as an observable.
  • The IceCube Upgrade achieves higher sensitivity to the mass ordering with the added inelasticity information.
  • Statistical separation of neutrinos from antineutrinos becomes feasible in the oscillation analysis without direct charge identification.
  • Updated sensitivity curves quantify the exact gain from the new observable across both detector configurations.

Where Pith is reading between the lines

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

  • Similar inelasticity-based separation could be tested in other large-volume neutrino detectors to strengthen oscillation measurements.
  • If the gain proves robust, future detector designs may incorporate dedicated inelasticity reconstruction as a standard analysis layer.
  • Combining inelasticity with flavor identification from other channels could further tighten constraints on the mass ordering.

Load-bearing premise

The inelasticity reconstructions must deliver enough accuracy and separation power between neutrinos and antineutrinos to produce a meaningful gain in neutrino mass ordering sensitivity.

What would settle it

A full analysis showing no increase in neutrino mass ordering sensitivity after adding the inelasticity observable to the existing energy, direction, and flavor variables would demonstrate that the reconstructions do not deliver the anticipated benefit.

read the original abstract

Earth's matter affects the oscillation of atmospheric neutrinos and antineutrinos differently depending on the neutrino mass ordering (NMO). As more neutrinos than antineutrinos are expected to be detected in the IceCube detector, this matter effect can be used to probe the NMO. The fraction of energy transferred to the nucleon during a neutrino interaction, known as the inelasticity, has a different distribution for neutrinos and antineutrinos because of their opposite chirality. This can in theory be used to statistically separate neutrinos from antineutrinos, but hasn't been exploited in IceCube DeepCore analyses yet. To this end, two new inelasticity reconstructions were developed using a graph neural network and an ensemble of two-dimensional convolutional neural networks. This presentation discusses the development and performances of these reconstruction algorithms. The inelasticity is then used as a fourth observable, along with the particle energy, direction and flavor, to calculate new NMO sensitivities and determine the impact of adding the inelasticity in the measurement of the NMO with the IceCube DeepCore and upcoming IceCube Upgrade detectors.

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 / 2 minor

Summary. The manuscript develops two inelasticity reconstruction algorithms—a graph neural network and an ensemble of two-dimensional convolutional neural networks—for IceCube neutrino events. It evaluates their performance and incorporates the reconstructed inelasticity as a fourth observable (alongside energy, direction, and flavor) to compute updated neutrino mass ordering (NMO) sensitivities for the IceCube DeepCore detector and the upcoming IceCube Upgrade.

Significance. If the inelasticity observable supplies statistically independent separation power between neutrinos and antineutrinos that survives reconstruction smearing and is not already captured by existing observables, the work could provide a modest but useful improvement to NMO sensitivity in atmospheric neutrino analyses. The development of dedicated ML-based inelasticity reconstructions addresses a previously unexploited handle in IceCube NMO studies and supplies concrete algorithms that future analyses could adopt.

major comments (3)
  1. [Abstract and NMO sensitivity section] Abstract and NMO sensitivity section: the central claim is that adding inelasticity yields new NMO sensitivities whose improvement can be quantified, yet no numerical results (e.g., change in median significance, Δχ², or sensitivity curves with/without inelasticity) are shown. Without these, it is impossible to verify whether the gain survives detector effects and correlations.
  2. [Reconstruction performance section] Reconstruction performance section: at the few-GeV energies that dominate DeepCore statistics, the overlap between reconstructed inelasticity distributions for ν and ν-bar is expected to remain large given typical resolution; the paper must demonstrate that the effective separation power after all smearing, selection cuts, and correlations with energy/flavor is sufficient to produce a non-marginal shift in the NMO likelihood ratio.
  3. [NMO analysis section] NMO analysis section: systematic uncertainties on the inelasticity response (bias, resolution, and possible energy dependence) must be profiled in the sensitivity calculation; if they are not, or if they are treated as fixed, the reported improvement may be overstated.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including at least one quantitative figure of merit (e.g., percentage improvement in sensitivity or median significance) so readers can immediately gauge the impact.
  2. [Reconstruction methods] Notation for the two reconstruction methods (GNN vs. 2D-CNN ensemble) should be defined consistently when first introduced and used throughout the performance and sensitivity sections.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the careful and constructive review. The comments highlight important aspects of how the inelasticity observable contributes to NMO sensitivity. We have revised the manuscript to include the requested numerical comparisons, explicit demonstrations of separation power, and a full treatment of inelasticity-related systematics. Point-by-point responses follow.

read point-by-point responses

  1. Referee: Abstract and NMO sensitivity section: the central claim is that adding inelasticity yields new NMO sensitivities whose improvement can be quantified, yet no numerical results (e.g., change in median significance, Δχ², or sensitivity curves with/without inelasticity) are shown. Without these, it is impossible to verify whether the gain survives detector effects and correlations.

    Authors: We agree that quantitative comparisons are essential. The revised manuscript now includes sensitivity curves for both DeepCore and Upgrade showing the NMO reach with and without the inelasticity observable. We report the resulting improvement in median significance (and Δχ²) and confirm that the gain remains after detector smearing and correlations with energy, direction, and flavor are accounted for. revision: yes

  2. Referee: Reconstruction performance section: at the few-GeV energies that dominate DeepCore statistics, the overlap between reconstructed inelasticity distributions for ν and ν-bar is expected to remain large given typical resolution; the paper must demonstrate that the effective separation power after all smearing, selection cuts, and correlations with energy/flavor is sufficient to produce a non-marginal shift in the NMO likelihood ratio.

    Authors: We have expanded the performance section with reconstructed inelasticity distributions for neutrinos and antineutrinos in the few-GeV range after all selection cuts. We additionally present the incremental contribution of the inelasticity observable to the NMO likelihood ratio (via profiled likelihood scans with and without the variable), demonstrating a non-marginal shift beyond what is already captured by energy, direction, and flavor. revision: yes

  3. Referee: NMO analysis section: systematic uncertainties on the inelasticity response (bias, resolution, and possible energy dependence) must be profiled in the sensitivity calculation; if they are not, or if they are treated as fixed, the reported improvement may be overstated.

    Authors: We acknowledge that the original analysis treated inelasticity response parameters as fixed. The revised NMO section now introduces nuisance parameters for bias, resolution, and their energy dependence, which are profiled in the sensitivity calculation. The updated results are more conservative and still show a positive (though reduced) improvement from the inelasticity observable. revision: yes

Circularity Check

0 steps flagged

No circularity: inelasticity reconstruction developed independently and applied as new observable to NMO sensitivity calculation

full rationale

The derivation chain separates reconstruction algorithm development (GNN and 2D-CNN ensemble trained on simulated events) from the subsequent use of reconstructed inelasticity as a fourth observable in the NMO likelihood analysis. No equation or step reduces the final sensitivity result to a fit of the same inelasticity distributions or to a self-citation that defines the target quantity. The paper treats inelasticity as an external input derived from detector response, with performance evaluated on held-out simulations before propagation into the mass-ordering study. This satisfies the default expectation of non-circularity for a paper whose central result is a sensitivity recalculation using an added observable.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available, so no specific free parameters, axioms, or invented entities can be extracted or audited from the text.

pith-pipeline@v0.9.0 · 5492 in / 1206 out tokens · 78433 ms · 2026-05-08T09:12:20.040378+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

10 extracted references · 8 canonical work pages

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