Implementing IceCube in SNOwGLoBES

Erin O'Sullivan (for the IceCube Collaboration); Felix Malmenbeck

REVIEW 3 major objections 4 minor 12 references

Implementing IceCube in SNOwGLoBES

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

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Pith's one-line read IceCube has been implemented in SNOwGLoBES, so researchers can compute its expected supernova neutrino event rates with the standard detector-comparison package.

desk verdict A useful, clearly described SNOwGLoBES module for IceCube that fills a real gap, but the 'working implementation' claim needs a benchmark against IceCube's own simulation or data before users can trust the predicted rates. read the letter →

arxiv 1909.00886 v2 pith:PR6DN4QG submitted 2019-09-02 astro-ph.HE astro-ph.IMphysics.ins-det

Felix Malmenbeck , Erin O'Sullivan (for the IceCube Collaboration) This is my paper · ORCID

classification astro-ph.HEastro-ph.IMphysics.ins-det

keywords IceCubeSNOwGLoBESsupernovaneutrinoseffectivevolumepost-smearingefficiencyinversebetadecaycore-collapsedetectorsimulation

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper reports a working implementation of IceCube in SNOwGLoBES, the software package used to estimate neutrino detection rates from core-collapse supernovae. The motivation is practical: SNOwGLoBES is a standard tool for comparing how different detectors would respond to a galactic supernova, but it had no IceCube simulation files. The implementation works by giving IceCube a fixed fiducial mass of 51,600 kilotonnes and folding the detector's energy-dependent effective volume into the post-smearing efficiency function. The authors demonstrate the result by computing IceCube signal time profiles for 20 supernova progenitor models under two equations of state. If the mapping is faithful, IceCube can now be included in routine SNOwGLoBES sensitivity comparisons without running a full detector simulation.

What carries the argument

The load-bearing object is the mapping between IceCube's effective volume and SNOwGLoBES's post-smearing efficiency function. In the SNOwGLoBES rate formula $R_I(t) = M_{\rm detector} n_{{\rm weight},I} (\Delta E)^2 \sum_{j,k} F_a(E_j) \sigma_I(E_j) k_I(E_k,E_j) T_I(E_k)$, the factor $T_I(E_k)$ normally describes the probability that an interaction product of energy $E_k$ triggers a detection. Here that factor is set equal to $\rho_{\rm ice} V_{\rm eff,tot}(E_k)/M$ with fixed $M = 51600$ kton, so the energy dependence of IceCube's detection efficiency enters through $T_I$ rather than through a varying detector mass. This lets the implementation reuse SNOwGLoBES's existing channel and cross-section machinery while still capturing the detector's energy-dependent response.

What would settle it

Take the same supernova flux used in the paper's demonstration and run a full Monte Carlo simulation of IceCube's DOM response (or use the published IceCube supernova analysis) to produce a hit-rate time series; compare it to the SNOwGLoBES calculation for the same input. Agreement within the expected statistical and systematic errors would support the implementation; a systematic divergence, especially at low neutrino energies or early times, would show that the constant-mass, effective-volume mapping in Eq. (3.2) is not adequate.

Watch

Extended reading notes

Core claim

The central claim is that IceCube has been successfully added to SNOwGLoBES, so that the expected supernova neutrino event rate in IceCube can be calculated with the package's standard tools. The technical workaround that makes this possible is Eq. (3.2): since SNOwGLoBES requires a single constant detector mass, the implementation sets $M = 51600$ kton and embeds all effective-volume information in the post-smearing efficiency $T_I(E_k) = \rho_{\rm ice} V_{\rm eff,tot}(E_k) / M$. The effective volume itself follows the IceCube supernova literature: $V_{\rm eff,tot}(E') = N_{\rm DOM} \varepsilon_{\rm noise} \theta(E' - E_{\rm ch}) (E' - E_{\rm ch}) C_{\pm} (dN_\gamma/dx) \langle V_{\rm eff,\gamma}\rangle$, with $N_{\rm DOM} = 0.98 \times 5160$ and $\varepsilon_{\rm noise} = 0.95$. Using SNOwGLoBES's standard water-Cherenkov channels, the authors demonstrate the implementation by calculating IceCube's expected signal for 20 supernova progenitor masses under normal and inverted mass hierarchies and two equations of state.

Load-bearing premise

The load-bearing premise is that IceCube's position- and energy-dependent response to supernova neutrinos can be represented in SNOwGLoBES by a fixed mass of 51,600 kilotonnes, an energy-dependent post-smearing efficiency derived from the effective volume, and a constant 0.95 deadtime factor, without biasing the calculated event rates.

Editorial extensions

If this is right

SNOwGLoBES users can now compute IceCube supernova neutrino event rates for arbitrary input flux spectra, not only precomputed models.
IceCube can be included alongside other detectors in SNOwGLoBES-based sensitivity comparisons for core-collapse supernovae.
A user-supplied noise-screening factor can be recovered by dividing the computed rates by the built-in constant 0.95.
The same effective-volume-to-efficiency procedure applies to other large water Cherenkov neutrino detectors, given an estimate of their effective volume.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Inference beyond the paper: The paper does not test Eq. (3.2) against a full IceCube event simulation, so the most useful next step would be a direct comparison of SNOwGLoBES output with published IceCube supernova rates under identical fluxes and deadtime assumptions.
Inference beyond the paper: Because $\varepsilon_{\rm noise} = 0.95$ is constant while real deadtime losses depend on the instantaneous DOM hit rate, the implementation likely overestimates the signal during the early, high-rate phase of a supernova; a time-dependent noise model would require extending SNOwGLoBES itself.
Inference beyond the paper: If merged into the standard SNOwGLoBES distribution, IceCube would become a default reference point in future supernova neutrino phenomenology, making detector-complementarity comparisons easier but also inheriting the systematic uncertainty of the effective-volume approximation.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Good to see this. The paper fills a real gap: SNOwGLoBES is the standard quick-comparison tool for supernova neutrino detectors, and IceCube was missing. The design choice in Eq. (3.2), folding the effective volume into the post-smearing efficiency with a fixed fiducial mass, is clean and sensible given SNOwGLoBES's fixed-mass constraint. The channel list and cross-sections lean on established work, and the code is in a pull request, so there are concrete artifacts. Credit where due: it's a useful, honest methods note.

The soft spot is exactly the one you'd expect: no benchmark. The paper claims a 'working implementation' but shows no comparison with IceCube's own published simulation or data, e.g. the rate curves from [3]. The single figure is just the implementation's own prediction. That matters because the mapping has known approximations. The constant deadtime factor epsilon_noise = 0.95 is a simplification; the time-dependent factor in [3] is epsilon_noise ~ 0.87/(1 + tau*r_SN(t)), which during the accretion peak can drop to ~0.7-0.85. So the implementation likely overpredicts signal by 10-35% in that phase. Also the assignment of effective volume to the post-smearing function is plausible but not cross-checked. These aren't fatal for a software paper—users can rescale and the structure is right—but the 'working' claim is stronger than the evidence.

The absence of a benchmark also makes it hard to debug. If a user's rate disagrees with IceCube's official sensitivity, they can't tell whether it's the effective volume parameterization, the deadtime simplification, or a coding mistake. A simple reproduction of the results from [3] with this SNOwGLoBES module would fix that. I'd suggest the authors add that, or at least clearly state the expected bias from the constant deadtime factor.

Bottom line: modest but genuinely useful contribution for the supernova neutrino community. It deserves proper review. I'd send it to a referee, with the request that the validation gap be addressed. The paper is a solid starting point for adding IceCube to SNOwGLoBES; right now it's a conditional acceptance.

Referee Report

3 major / 4 minor

Summary. The paper presents an implementation of IceCube in the SNOwGLoBES package for computing supernova neutrino detection rates. To work within SNOwGLoBES's fixed-mass framework, the authors replace IceCube's position-dependent sensitivity with an effective volume V_eff,tot and absorb the product rho_ice*V_eff,tot into the post-smearing efficiency T_I(E_k) via Eq. (3.2), using a constant fiducial mass M=51600 kton, a constant deadtime efficiency epsilon_noise=0.95, and an assumed 98% DOM livetime. The implementation includes inverse beta decay, neutrino-electron scattering, and oxygen channels, with cross-section choices listed in Table 1. A sample calculation for 20 supernova progenitor masses is shown in Figure 1. The code has been submitted as pull request #8 to the SNOwGLoBES repository.

Significance. If validated, this implementation would fill a genuine gap in SNOwGLoBES by enabling direct, fast comparisons between IceCube and other supernova neutrino detectors within a widely used community tool. The effective-volume-to-efficiency mapping is a clean workaround for SNOwGLoBES's inability to vary detector mass with energy or channel, and the approach is potentially generalizable to other large water Cherenkov detectors such as KM3Net. The submitted code as a pull request is a concrete reproducibility asset. However, the paper's central claim of a 'working implementation' currently rests on an unvalidated effective-volume mapping and an unquantified deadtime approximation, so the practical significance is conditional on the benchmarks and sensitivity studies requested below.

major comments (3)

[Section 4 and Section 3.3 (Eq. 3.2)] The claim that this is a 'working implementation' (Section 4) is not supported by any benchmark against IceCube's published supernova rates or full detector simulation, e.g., reference [3]. The only quantitative output, Figure 1, is a self-prediction rather than a validation. Because Eq. (3.2) absorbs the effective volume into the post-smearing efficiency, any discrepancy between the SNOwGLoBES output and IceCube's published rates could originate from the effective-volume parametrization, the deadtime simplification, or an implementation error. The manuscript should compare its output with the rates in [3] (or an equivalent reference simulation) for a common supernova model and show agreement within the expected systematic uncertainties.
[Section 3.2 and Eq. (2.1)] The constant deadtime efficiency epsilon_noise = 0.95 replaces the time-dependent expression epsilon_noise ~ 0.87/(1 + tau*r_SN(t)) from [3]. During the accretion phase, where the per-DOM supernova rate can be several hundred Hz to 1 kHz, the time-dependent factor is expected to drop to roughly 0.7-0.85, so the constant factor likely overpredicts the signal by 10-35%. The paper should quantify this bias for the models shown in Figure 1, and should either provide a mechanism for users to supply a time-dependent deadtime factor or explicitly restrict the validity of the default constant value.
[Section 3.1 and Section 3.3 (Table 1, Eq. 3.2)] The generation of the post-smearing efficiency functions T_I(E_k) for the inverse beta decay and neutrino-electron scattering channels is not described in sufficient detail for reproducibility. The text says these functions were 'generated using the sources listed in Table 1' but does not specify the exact functional form that converts V_eff,tot(E_k) into T_I(E_k) for each channel, nor how the relationship between the incoming neutrino energy E_j and the produced electron/positron energy E_k is handled in the summation of Eq. (2.4). The authors should provide the explicit algorithm or code so that a user can verify the implementation independently.

minor comments (4)

[Section 3.3] The fiducial mass M = 51600 kton is an arbitrary normalization scale, not the physical mass of IceCube; the text should state more prominently that the choice of M cancels in the effective rate through Eq. (3.2), to prevent users from misinterpreting this value as IceCube's detector mass.
[Section 2.2] The relationship between N_gamma(E') in Eq. (2.1) and <V_eff,pm>(E') in Eq. (2.3) is not explicit; a reader may be confused about whether N_gamma(E') is given by theta(E'-E_ch)*(E'-E_ch)*C_pm*dN_gamma/dx or is an additional factor. Please clarify the definition of N_gamma(E').
[Figure 1] The caption should specify the assumed supernova distance, the luminosity normalization, and whether the shown rates include the constant deadtime factor epsilon_noise = 0.95, so that the predictions can be compared with other calculations.
[Section 3.2] The sentence 'recent advances such as the development of HitSpooling is expected to allow' contains a subject-verb agreement error; it should read 'are expected to allow.'

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the IceCube implementation is an algebraic renormalization of external effective-volume and cross-section inputs with no fitted parameters.

full rationale

The derivation chain is self-contained: SNOwGLoBES rate Eq. (2.4) uses a fixed detector mass M and a post-smearing efficiency T_I(E_k). The paper defines T_I(E_k) = rho_ice V_eff,tot(E_k)/M (Eq. 3.2), so when substituted into Eq. (2.4) the arbitrary mass M cancels exactly and the computed rate is simply the effective-volume rate supplied by Eq. (2.2), whose ingredients (N_DOM, eps_noise, N_gamma, <V_eff,+->) are taken from published simulations ([3], [7]) rather than fitted to any IceCube event sample. No parameter is adjusted to reproduce a target output; the constant deadtime factor 0.95 and N_DOM = 0.98*5160 are explicit modeling assumptions, and the absence of a validation benchmark is a correctness/robustness concern, not circularity. The self-citations to IceCube papers [3],[4] supply noise rates and deadtime context, but the central conversion in Eq. (3.2) does not depend on an unverified uniqueness theorem or on a claim whose content is identical to its conclusion. The paper therefore contains no self-definitional, fitted-input, or citation-driven circular step.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The implementation depends on external effective volume and cross-section models plus several hand-chosen constants. No new physics entities are introduced. The main unvalidated step is the encoding of effective volume into a post-smearing efficiency.

free parameters (3)

constant deadtime efficiency factor epsilon_noise = 0.95
Chosen as a constant to approximate the time-dependent noise screening in SNOwGLoBES, which lacks dynamic noise support. Stated in Section 3.2.
operational DOM fraction = 0.98 x 5160
NDOM(t)/5160 = 0.98, chosen for consistency with [3] (Section 3.3).
fiducial detector mass M = 51600 kton
Set to correspond to about 10^4 m^3 per DOM; used as constant scale factor in SNOwGLoBES (Section 3.3).

assumptions (5)

domain assumption Eq. (2.1), adapted from [3], accurately describes IceCube's supernova neutrino detection rate.
Starting formula for the implementation; not re-derived or validated in this paper.
domain assumption The effective volume parameterization in Eqs. (2.2)-(2.3), with constants from [7], correctly represents IceCube's sensitivity to low-energy electrons and positrons.
The specific numerical values (Cplus, Cminus, dN_gamma/dx, average effective photon volume) are imported from the Kowarik thesis without independent verification.
domain assumption The cross sections listed in Table 1, from [9,10] and the SNOwGLoBES v1.2 oxygen files, are valid for supernova neutrino energies.
Used without re-derivation; selected to agree with [3].
domain assumption A constant detector mass M and a post-smearing efficiency T_I containing the full effective volume is an acceptable way to represent IceCube in GLoBES.
Workaround in Eq. (3.2) is asserted, not proven equivalent to standard GLoBES usage.
domain assumption 98% of DOMs are operational and the ice density is 910 kg/m^3.
Stated assumptions in Section 3.3; the DOM fraction is chosen for consistency with [3].

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Cite this review

Pith. "Pith review of Implementing IceCube in SNOwGLoBES." pith.science (2026). https://pith.science/paper/PR6DN4QG

@misc{pith2026190900886,
  author       = {Pith},
  title        = {Pith review of: Implementing IceCube in SNOwGLoBES},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PR6DN4QG}},
  note         = {Machine review of arXiv:1909.00886}
}

read the original abstract

We present an implementation of IceCube in the SNOwGLoBES package, which is used to calculate expected detection event rates resulting from supernova neutrinos. The SNOwGLoBES package is widely used to compare the sensitivity of different neutrino observatories, but currently does not include simulation files for IceCube. In this paper, we give a brief overview of the design process that went into this implementation.

Figures

Figures reproduced from arXiv: 1909.00886 by the authors.

**Figure 1.** Comparing the time evolution of IceCube signals for 20 different supernova progenitor masses, assuming either the Lattimer-Swesty (left) or HShen (right) equation of state. The solid (dashed) lines show expected detection rates assuming the normal (inverted) hierarchy. Ingoing fluxes were provided by [2], and the expected detection rates were calculated using SNOwGLoBES. 1.2 IceCube IceCube is a cubic-kilometer neut… view at source ↗

**Figure 2.** Figures from [3], illustrating how IceCube’s sensitivity to low-energy neutrino interactions varies with the location of the event. Left: The spatial distribution of detected supernova neutrino interaction events, simulated using GEANT-3.21. Each dot represents a interaction event which was detected by a digital optical module. Right: The effective volume V eff γ for detecting Cherenkov photonswith wavelength (300 -… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

12 extracted references · 8 canonical work pages

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[9]

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write newline

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Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.

[1] [3]

Abbasi et al

The IceCube Collaboration: R. Abbasi et al. ASTRONOMY & ASTROPHYSICS, 535:109 (2011)

work page 2011

[2] [1]

Albert et al., 2018

J. Albert et al., 2018. https://github.com/SNOwGLoBES/snowglobes/blob/master/doc/snowglobes_1.2.pdf https://github.com/SNOwGLoBES/snowglobes/blob/master/doc/snowglobes\_ 1.2.pdf [ accessed 2019-07-13 ]

work page 2018

[3] [2]

O'Connor & C.D

E. O'Connor & C.D. Ott. ApJ 762 126 (2013). [ arXiv:1207.1100v2 ] \\ Dataset: https://sntheory.org/M1prog https://sntheory.org/M1prog [ accessed 2018-12-09 ]

arXiv 2013

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The IceCube Collaboration, PoS(ICRC2019)889 (these proceedings)

work page

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Huber et al., 2005

P. Huber et al., 2005. Comput. Phys. Commun. 167 (2005) 195. [ arXiv:hep-ph/0407333 ]

arXiv 2005

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Huber et al., 2007

P. Huber et al., 2007. Comput. Phys. Commun. 177 (2007). [ arXiv:hep-ph/0701187 ]

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Kowarik, 2010

T. Kowarik, 2010. Ph.D. thesis, Mainz U., Inst. Phys

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Malmenbeck, 2019

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