Pith. sign in

REVIEW 3 major objections 4 minor 32 references

Detector-level simulation closure of radio air-shower reconstruction with native RF-chain inversion and out-of-fold endpoint interpolation

T0 review · 3 major / 4 minor · reviewed 2026-08-02 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Trigger-selected radio voltage traces alone yield air-shower energies with about 11% scatter and near-zero mean bias in simulated events, with no fitted scale.

desk verdict A careful, honest matched-simulation closure for RF-chain radio reconstruction; read the zenith-resolved tables rather than the abstract mean. read the letter →

arxiv 2607.10637 v2 pith:P66JA2IZ submitted 2026-07-12 astro-ph.IM astro-ph.HE

Xin Xu , Pengfei Zhang , Fufu Yang , Xiaosong Liu , Chao Zhang This is my paper · ORCID
classification astro-ph.IMastro-ph.HE
keywords ultra-high-energycosmicraysextensiveairshowersradiodetectionair-showerreconstructionRF-chaininversiondetector-levelsimulationclosureenergyout-of-foldvalidation
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 attempts to show that an autonomous radio array can reconstruct cosmic-ray air-shower direction and energy starting from the same kind of input a real detector would deliver—trigger-selected ADC voltage traces—rather than from idealized electric-field footprints. In a matched simulation, 693 quality-selected events with reconstructed zenith between 60° and 85° have mean energy residuals between -0.66% and -0.05% and event-to-event scatter between 10.75% and 11.04% across three waveform branches: raw-noisy weighted, hard-gated denoised equal-weight, and clean equal-weight reference. The hard-gated branch has a median angular separation of 0.052°. These numbers are produced by inverting the radio-frequency chain on its native frequency grid and blending iron- and proton-endpoint template fits with a cross-fitted interpolation weight, all without a fitted multiplicative energy scale. The paper is careful that this is cross-fitted closure within one simulation framework, not deployed-array resolution or an independent calibration; the upper-zenith boundary (85–90°) fails with +14.2% mean residual and 19.9% scatter.

What carries the argument

The load-bearing mechanism is native RF-chain inversion on each event's own discrete Fourier grid, restricted to a 50–200 MHz passband, which recovers electric-field waveforms from digitized voltages and yields the station observable |Exπ|, the peak amplitude projected along the shower-plane v×B axis. Energy estimation then uses symmetric four-arm lateral-distribution templates built from simulated iron and proton footprints at one reference energy, radio-core recentered by a refractive-shift proxy; a nested five-fold out-of-fold logistic-regression weight interpolates between the two endpoint energies so that no event is predicted by a model trained on it. The four-arm profile is the key si

What would settle it

Split the 693-event primary sample by true azimuth: if mean energy residuals vary systematically across azimuth, especially far from the template azimuth, beyond the reported bootstrap intervals, the azimuth-collapsed template assumption fails. A cleaner test would be to run the same pipeline with an independent shower-simulation code and check whether the near-zero mean residual persists or shifts.

Watch

Extended reading notes

Core claim

The central claim is that the detector-level inverse problem for radio air showers is solvable: from L1-triggered packages of noisy ADC traces, a pipeline of voltage-domain geometry, native-grid RF-chain inversion, 50–200 MHz v×B peak-amplitude extraction, symmetric four-arm iron/proton template fits, and nested five-fold out-of-fold endpoint interpolation reconstructs energy and direction with small mean bias and about 11% scatter across 693 common events in the 60–85° zenith band, without fitting any amplitude or energy scale. The paper also claims that the raw-noisy and hard-gated branches match the clean reference to within a few tenths of a percent in paired mean difference, meaning the

Load-bearing premise

The energy closure rests on the assumption that one azimuth-collapsed, four-arm v×B peak-amplitude template family—generated at a single reference azimuth and energy, with a refractive radio-core shift proxy and no charge-excess correction—adequately describes every event in the 60–85° band, and the paper's own +14.2% upper-boundary residual marks where that assumption begins to break.

Editorial extensions

If this is right

  • If the closure is representative, autonomous arrays can reconstruct shower energy and direction from raw triggered traces without requiring ideal electric-field footprints, a fitted energy scale, or per-event composition labels.
  • The small paired branch differences (0.24–0.60 percentage points relative to clean) imply that denoising and noise-weighting do not materially change the energy result; the roughly 11% scatter is dominated by shower-to-shower and template-model effects, not by electronic noise.
  • Fixed iron-only or proton-only templates carry opposite mean biases (+8.5% and -8.3% in the denoised branch); out-of-fold interpolation reduces the composition-averaged offset to near zero, so the interpolation mechanism is what makes the single estimator usable over the full angular range.
  • The sharp upper-boundary failure (85–90°: +14.2% mean, 19.9% scatter) sets an explicit validity limit: the same pipeline and templates should not be applied beyond 85° without a two-dimensional or physically corrected footprint model.
  • The median angular separation of about 0.05° is a matched-simulation closure value; adding deployed-array timing, positioning, and calibration systematics will degrade it, so it should not be read as field angular resolution.

Reading between the lines

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

  • Inference: Because the template family is generated at a single azimuth and one reference energy, the closure likely degrades with distance from that azimuth; a true-azimuth split of the 693-event sample would reveal whether charge-excess asymmetry not captured by the azimuth-collapsed profile is hiding in the 11% scatter.
  • Inference: The same out-of-fold interpolation architecture could be retrained on two-dimensional radio-footprint templates with explicit energy and shower-development dependence; if that eliminates the upper-boundary offset without a fitted scale, the approach would extend to near-horizontal showers and potentially yield composition-sensitive energy estimates.
  • Inference: Applying the pipeline to an independent shower generator or a different site's radio-frequency chain would test transferability; a shifted mean residual would quantify how much of the closure is specific to the matched simulation framework.
  • Inference: The near-zero mean residual across all three waveform branches suggests that a fluence-based energy estimator, once its signal window and noise subtraction are calibrated, may achieve comparable or better scatter; comparing peak-amplitude and fluence estimators within this detector-level closure would settle which observable is more noise-tolerant.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 4 minor

Summary. This paper presents a detector-level simulation-closure test for radio air-shower reconstruction. 972 ZHAireS event packages (QGSJET-II-04, proton/iron, 45–89 deg, 0.001–4 EeV) are propagated through an RF chain and trigger; reconstruction uses a voltage-domain ADF plus joint timing–amplitude axis fit, native-grid FFT inversion to the 50–200 MHz electric field, a peak |E_xπ| observable along the shower-plane v×B axis, and fixed symmetric four-arm iron/proton templates at reference energy 0.316 EeV. A nested five-fold out-of-fold logistic-regression weight interpolates between the Fe and p endpoint energies. On a common-quality sample of 725 events (693 with reconstructed zenith 60–85 deg), the three waveform branches (raw-noisy weighted, hard-gated denoised equal-weight, clean equal-weight) achieve mean energy residuals of -0.05%, -0.30%, -0.66% with event-to-event scatter 10.75–11.04%, and a median angular separation of about 0.052 deg. Boundary zenith ranges are reported separately, with the 85–90 deg bin showing a mean residual of +14.2% and scatter 19.9%. The paper explicitly restricts its claims to matched-simulation, cross-fitted closure and does not claim deployed-array calibration or independent energy resolution.

Significance. If the claims hold, this is a useful methodological benchmark: it shows that a trigger-to-energy reconstruction chain can operate without a global calibration constant in a controlled simulation, with reproducible data/scripts, bootstrap intervals, and an honest report of boundary failures. The strengths are the transparent cross-fitting design, the explicit statement of limitations, the separate reporting of edge domains, and the availability of code/data to regenerate figures and tables. However, the central 'near-zero mean residual' result currently rests on an aggregate mean that averages over a strong within-domain zenith trend, and the 'no fitted multiplicative energy scale' wording is contradicted by the per-event amplitude fit in Eq. (3.6). These issues are fixable but require reworking the presentation and the main interpretive claims.

major comments (3)
  1. [§3.5, Eq. (3.6); Abstract] Eq. (3.6) performs a per-event least-squares amplitude fit â_k and sets E_k = 0.316 â_k EeV. The text immediately after says 'No global, primary-dependent, event-dependent or zenith-dependent amplitude scale is fitted,' which is internally inconsistent: â_k is an event-dependent multiplicative amplitude scale. The abstract's claim of doing reconstruction 'without a fitted multiplicative energy scale' is therefore misleading. What the analysis actually avoids is a global calibration constant or a post-fit multiplicative correction. Please reword this central claim precisely.
  2. [§4.4, Table 6; Abstract] The headline mean residuals of -0.05% to -0.66% are aggregates over the 60–85 deg domain. Table 6 shows a systematic zenith trend within this domain: -5.1% (60–70), -1.4% (70–75), +5.3% (75–80), +7.1% (80–85). The near-zero aggregate mean is thus a cancellation of opposing biases whose relative weights depend on the trigger-selected zenith distribution, not evidence of per-zenith template validity. Please present zenith-differential residuals as the primary closure statistic (or reweight to a stated zenith distribution) and temper the abstract's emphasis on the aggregate mean.
  3. [§3.4, Eq. (3.5); §4.4] The template family is azimuth-collapsed: generated only at azimuth 45°, at one reference energy, and averaged symmetrically over four arms. No residual-versus-true-azimuth analysis is provided, although geomagnetic-angle and charge-excess interference vary with azimuth. The upper-boundary failure and the +7.1% mean at 80–85 deg suggest the one-dimensional v×B peak profile already loses accuracy inside the primary domain. Add a diagnostic of residuals versus true azimuth (and ideally true energy) to support the claim that this template family is adequate for the entire 60–85 deg primary domain.
minor comments (4)
  1. [§2.2] 'The trigger configuration uses the horizontalychannel' appears to be a typo; it should be 'horizontal y channel'.
  2. [Eq. (3.4) and Sec. 3.4] The symbol p is used for the integration variable in Eq. (3.4) and also as the proton-endpoint label (p) and the parameter p = n_src sinθ. This triple use is confusing; consider renaming the endpoint label or the parameter.
  3. [§1] The term 'GP80-like' is not defined or referenced. Provide a citation or a brief description of GP80 so the reader can understand the intended array concept.
  4. [Figure 5] The panel titles report true zenith for display, but the text should state explicitly why reconstructed zenith is not used in the titles, to avoid confusion with the primary-domain definition in Eq. (3.9).

Circularity Check

3 steps flagged · score 4.0 of 10

Matched-simulation closure is transparently labeled as such; residual risk is the single-anchor template/refractive-proxy chain and the self-cited FARSim methodology serving as the template's only provenance.

  1. other [Section 3.4, Eqs. (3.4)–(3.5); Section 2.1; Section 5]
    "At large zenith angle, the center of the radio footprint need not coincide with the straight particle-axis ground intersection because atmospheric refraction ... For source height h and reconstructed zenith θ, the horizontal shift proxy is Δrcore = h tanθ − ∫_0^h sqrt(n(z)^2 − p^2) dz ... The template library contains fixed iron and proton families generated from ZHAireS star-shaped footprints following the FARSim methodology [20]. ... The one-dimensional v×B endpoint profile is formed symmetrically from four principal shower-plane arms, M_k(ρ) = 1/4 [E^k_xπ(ρ,0°) + E^k_xπ(ρ,90°) + E^k_xπ(ρ,18"

    The paper does not claim this template family is a derived first-principles result; it states the OOF interpolation uses fixed templates generated following the FARSim methodology and gives the azimuth-collapsed, reference-energy, two-primary template as the specified input. The remaining risk is that the central energy-closure claim depends on that input family, whose only provenance is a same-author arXiv preprint [20]. The paper's own upper-boundary diagnostic admits the one-dimensional v×B peak-amplitude profile is 'no longer sufficient' at 85–90°, so within the stated angular domain the claim is a conditional closure of the pipeline under that input.

  2. other [Section 3.4, Eq. (3.4)]
    "The event and template coordinates therefore use the same refractive radio-core recentering convention. For source height h and reconstructed zenith θ, the horizontal shift proxy is Δrcore = h tanθ − ∫_0^h sqrt(n(z)^2 − p^2) dz, p = n_src sinθ, where n_src = n(h) is the refractive index at the fitted source height."

    The recentering proxy is explicitly described as an approximation used identically for events and templates, rather than as a measured radio-core displacement. Because both sides of the closure share the same coordinate convention, any bias in the proxy cancels by construction in the aggregate residual, and the reported closure can only validate the proxy partially, through the upper-boundary residual, which the paper shows fails. This is a shared-input convention, not a prediction of an independent quantity.

1 more flagged steps
  1. other [Section 4.1 and Table 6]
    "The common quality sample is defined before evaluating the energy estimator. ... The primary analysis domain is then defined using only the reconstructed direction: 60° ≤ θ_rec < 85°. ... In the hard-gated denoised branch the mean residuals are −5.1%, −1.4%, +5.3% and +7.1% in the four bins from 60° to 85°, while the scatter grows to 11.4% in the 80°–85° bin."

    The claim that the near-zero aggregate mean residual is a successful closure is a statement about a domain selected after inspecting the residual structure; the paper itself reports structured bin-to-bin residuals (−5.1% to +7.1%) that are large compared with the aggregate −0.30% and that cancel in the wide 60–85° average. This is not circularity-by-definition, but the aggregate headline is partly produced by the chosen domain width and the composition-averaging of the OOF interpolation, not by an independent demonstration that the template family is unbiased at every zenith.

full rationale

By the standard for 'circularity' as derivation equivalent by construction to its inputs, this paper is largely non-circular. The headline residuals are the output of an end-to-end pipeline, not a fitted parameter renamed as a prediction: no multiplicative energy scale is fit, and the OOF interpolation excludes each event's own primary label and true energy. The paper repeatedly and accurately labels the result as a matched-simulation cross-fitted closure, and it quantifies its own limitations (structured zenith residuals, +14.2% upper-boundary failure, iron/proton offsets). The main reason the score is not 0–2 is that the central energy closure depends on a template library whose construction is attributed to the authors' own FARSim preprint [20] and is generated only at one azimuth and one reference energy; the azimuth-collapsed template (Eq. 3.5) and the same-convention radio-core proxy (Eq. 3.4) are shared inputs to both events and templates, so the closure partially validates the consistency of the pipeline with its own assumptions rather than independently testing those assumptions. The self-citation is not used to forbid alternatives, however, and the paper's own boundary diagnostic provides external falsifiability within the framework. Thus score 4: some self-citation and shared-template structure, but the central claim retains independent content as a detector-level closure test.

Assumptions & free parameters 6 free parameters · 6 assumptions · 0 invented entities

The central claim depends on several domain assumptions inherited from the simulation framework and on the adequacy of the fixed template library and OOF interpolation. There are no new physical entities. The primary free parameters are per-event geometry fits, the loss weight, the regularization hyperparameter, and hand-selected quality thresholds; none is a global fitted energy scale, but several influence the composition of the 693-event sample.

free parameters (6)
  • ADF Cherenkov angle omega_c
    Fitted per event in the voltage-domain geometry model, Eq. 3.1.
  • ADF width parameter Delta_omega
    Appears in Eq. 3.1 as a shape parameter; the text does not specify how it is set.
  • ADF amplitude-loss weight = 0.005
    Hand-fixed constant in the joint timing-amplitude loss, §3.2.
  • OOF logistic regularization C = 10
    Selected by inner-fold Brier score from C in {0.03,0.1,0.3,1,3,10}; all outer folds selected C=10, Appendix B.
  • Quality-selection thresholds = Nfit>=5, rsph<30ns, R2>=0.50/0.75, 60-85 deg domain
    Hand-chosen selection criteria in §3.5 and Eq. 3.9; they define the 693-event primary sample.
  • Source height h and n_src in radio-core shift
    Used in Eq. 3.4 to compute the refractive core displacement; how h is fixed or fitted is not fully described.
assumptions (6)
  • domain assumption ZHAireS with QGSJET-II-04 and the GDAS atmosphere gives valid radio-emission traces for air-showers.
    Used to produce all 972 event packages and the template library, §2.1.
  • domain assumption The RF-chain transfer function and the complex effective-length matrix are known exactly for inversion.
    Native inversion in §3.3 assumes the electronics response can be deconvolved without uncertainty.
  • domain assumption Templates generated at reference energy 0.316 EeV, azimuth 45°, 2 ns bins, and four principal arms are applicable to all events after geometric transformation.
    Template construction and recentering in §3.4 assumes this fixed library spans the phase space.
  • domain assumption The refractive radio-core shift, Eq. 3.4, is an adequate coordinate approximation without full ray tracing.
    Used for both events and templates; paper notes it is a proxy, not a measured particle-core displacement.
  • ad hoc to paper The one-dimensional symmetric four-arm v x B profile, Eq. 3.5, retains enough footprint information for energy reconstruction without charge-excess correction.
    This is the load-bearing template ansatz; the paper's own upper-boundary failure at 85-90 deg indicates where it breaks.
  • domain assumption The logistic-regression feature groups in Appendix B are sufficient for endpoint interpolation, and the class-balanced training targets reflect the simulated sample.
    Defines the OOF interpolation weight p_p; no independent composition prior is used.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Detector-level simulation closure of radio air-shower reconstruction with native RF-chain inversion and out-of-fold endpoint interpolation." pith.science (2026). https://pith.science/paper/P66JA2IZ

@misc{pith2026260710637,
  author       = {Pith},
  title        = {Pith review of: Detector-level simulation closure of radio air-shower reconstruction with native RF-chain inversion and out-of-fold endpoint interpolation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/P66JA2IZ}},
  note         = {Machine review of arXiv:2607.10637}
}
read the original abstract

Autonomous radio arrays reconstruct air showers from trigger-selected digitized voltage traces rather than ideal electric-field footprints. We present a detector-level simulation-closure test linking RF-chain-triggered ZHAireS event packages to reconstructed direction, energy and quality diagnostics. Voltage amplitudes and station timing determine geometry through a robust joint timing--amplitude axis fit. Native-grid RF-chain inversion recovers 50--200 MHz electric-field peaks along the shower-plane v cross B axis. Symmetric four-arm iron and proton templates generated with 2 ns time bins provide endpoint estimates that are combined by a nested five-fold out-of-fold interpolation estimator. One estimator is used over the full angular range without a fitted multiplicative energy scale. Of 972 triggered event packages, 725 pass common quality selection in raw-noisy weighted, hard-gated denoised equal-weight and clean equal-weight branches. In the primary reconstructed-zenith range 60--85 degrees, 693 common events have mean energy residuals of -0.05, -0.30 and -0.66 percent with standard deviations of 11.04, 10.90 and 10.75 percent. The hard-gated denoised branch has a median angular separation of 0.052 degrees. Boundary zenith ranges are reported separately; the upper boundary has a mean energy residual of +14.2 percent and a 19.9 percent standard deviation. These results describe cross-fitted closure within a matched simulation framework, not deployed-array resolution or independent energy calibration.

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

32 extracted references · 3 canonical work pages

  1. [1]

    Huege,Radio detection of cosmic ray air showers in the digital era, Phys

    T. Huege,Radio detection of cosmic ray air showers in the digital era, Phys. Rept.620 (2016) 1–52, doi:10.1016/j.physrep.2016.02.001

    doi:10.1016/j.physrep.2016.02.001 2016

  2. [2]

    F. G. Schroder,Radio detection of cosmic-ray air showers and high-energy neutrinos, Prog. Part. Nucl. Phys.93(2017) 1–68, doi:10.1016/j.ppnp.2016.12.002

    doi:10.1016/j.ppnp.2016.12.002 2017

  3. [3]

    F. G. Schroder,Radio detection of ultrahigh-energy cosmic-ray air showers, Eur. Phys. J. Spec. Top.234(2025) 4957–4966, doi:10.1140/epjs/s11734-025-01503-4

    doi:10.1140/epjs/s11734-025-01503-4 2025

  4. [4]

    Aabet al.[Pierre Auger Collaboration],Probing the radio emission from air showers with polarization measurements, Phys

    A. Aabet al.[Pierre Auger Collaboration],Probing the radio emission from air showers with polarization measurements, Phys. Rev. D89(2014) 052002, doi:10.1103/PhysRevD.89.052002

    work page doi:10.1103/physrevd.89.052002 2014

  5. [5]

    Schellartet al.,Polarized radio emission from extensive air showers measured with LOFAR, JCAP10(2014) 014, doi:10.1088/1475-7516/2014/10/014

    P. Schellartet al.,Polarized radio emission from extensive air showers measured with LOFAR, JCAP10(2014) 014, doi:10.1088/1475-7516/2014/10/014

    doi:10.1088/1475-7516/2014/10/014 2014

  6. [6]

    Aabet al.[Pierre Auger Collaboration],Energy estimation of cosmic rays with the Engineering Radio Array of the Pierre Auger Observatory, Phys

    A. Aabet al.[Pierre Auger Collaboration],Energy estimation of cosmic rays with the Engineering Radio Array of the Pierre Auger Observatory, Phys. Rev. D93(2016) 122005, doi:10.1103/PhysRevD.93.122005

    doi:10.1103/physrevd.93.122005 2016

  7. [7]

    Aabet al.[Pierre Auger Collaboration],Measurement of the radiation energy in the radio signal of extensive air showers as a universal estimator of cosmic-ray energy, Phys

    A. Aabet al.[Pierre Auger Collaboration],Measurement of the radiation energy in the radio signal of extensive air showers as a universal estimator of cosmic-ray energy, Phys. Rev. Lett.116(2016) 241101, doi:10.1103/PhysRevLett.116.241101

    doi:10.1103/physrevlett.116.241101 2016

  8. [8]

    Buitinket al.,Method for high precision reconstruction of air showerXmax using two-dimensional radio intensity profiles, Phys

    S. Buitinket al.,Method for high precision reconstruction of air showerXmax using two-dimensional radio intensity profiles, Phys. Rev. D90(2014) 082003, doi:10.1103/PhysRevD.90.082003

    doi:10.1103/physrevd.90.082003 2014

Show all 32 references
  1. [9]

    P. A. Bezyazeekovet al.[Tunka-Rex Collaboration],Radio measurements of the energy and the depth of the shower maximum of cosmic-ray air showers by Tunka-Rex, JCAP01(2016) 052, doi:10.1088/1475-7516/2016/01/052

    2016 doi

  2. [10]

    Kostunin, P

    D. Kostunin, P. A. Bezyazeekov, R. Hiller, F. G. Schroder, V. Lenok and E. Levinson, Reconstruction of air-shower parameters for large-scale radio detectors using the lateral distribution, Astropart. Phys.74(2016) 79–86, doi:10.1016/j.astropartphys.2015.10.004

    2016 doi

  3. [11]

    Buitinket al.,A large light-mass component of cosmic rays at1017–1017.5 eV from radio observations, Nature531(2016) 70–73, doi:10.1038/nature16976

    S. Buitinket al.,A large light-mass component of cosmic rays at1017–1017.5 eV from radio observations, Nature531(2016) 70–73, doi:10.1038/nature16976

    2016 doi

  4. [12]

    P. A. Bezyazeekovet al.[Tunka-Rex Collaboration],Measurement of cosmic-ray air showers with the Tunka Radio Extension (Tunka-Rex), Nucl. Instrum. Meth. A802(2015) 89–96, doi:10.1016/j.nima.2015.08.061

    2015 doi

  5. [13]

    P. A. Bezyazeekovet al.[Tunka-Rex Collaboration],Reconstruction of cosmic ray air showers with Tunka-Rex data using template fitting of radio pulses, Phys. Rev. D97(2018) 122004, doi:10.1103/PhysRevD.97.122004

    2018 doi

  6. [14]

    Alvarez-Munizet al.[GRAND Collaboration],The Giant Radio Array for Neutrino Detection (GRAND): Science and design, Sci

    J. Alvarez-Munizet al.[GRAND Collaboration],The Giant Radio Array for Neutrino Detection (GRAND): Science and design, Sci. China Phys. Mech. Astron.63(2020) 219501, doi:10.1007/s11433-018-9385-7. – 22 –

    2020 doi

  7. [15]

    Alvarez-Muniz, W

    J. Alvarez-Muniz, W. R. Carvalho Jr. and E. Zas,Monte Carlo simulations of radio pulses in atmospheric showers using ZHAireS, Astropart. Phys.35(2012) 325–341, doi:10.1016/j.astropartphys.2011.10.005

    2012 doi

  8. [16]

    Huege, M

    T. Huege, M. Ludwig and C. W. James,Simulating radio emission from air showers with CoREAS, AIP Conf. Proc.1535(2013) 128–132, doi:10.1063/1.4807534

    2013 doi

  9. [17]

    Zilleset al.,Radio Morphing: towards a fast computation of the radio signal from air showers, Astropart

    A. Zilleset al.,Radio Morphing: towards a fast computation of the radio signal from air showers, Astropart. Phys.114(2020) 10–21, doi:10.1016/j.astropartphys.2019.06.001

    2020 doi

  10. [18]

    Desmet, S

    M. Desmet, S. Buitink, T. Huege and K. Watanabe,SMIET: Fast and accurate synthesis of radio pulses from extensive air shower using simulated templates, Astropart. Phys.175 (2026) 103182, doi:10.1016/j.astropartphys.2025.103182

    2026

  11. [19]

    Lenok and F

    V. Lenok and F. G. Schroder,A probabilistic model for the efficiency of cosmic-ray radio arrays, JCAP06(2023) 014, doi:10.1088/1475-7516/2023/06/014

    2023 doi

  12. [20]

    X. Xu, P. Zhang, F. Yang, P. Ma, R. Koirala and C. Zhang,FARSim: a fast RF-chain-aware trigger-screening surrogate for radio detection of ultra-high-energy cosmic rays, arXiv preprint arXiv:2605.28373 [astro-ph.IM] (2026)

    2026 arXiv

  13. [21]

    Zhang, X

    P. Zhang, X. Xu, H. Wang, X. Xu, B. Duan, F. Wei, H. Pan, X. Tian, Y. Zhang, P. Ma and O. Martineau-Huynh,Noise suppression and radio frequency interference rejection for self-triggered radio detectors of extensive air showers, IEEE Trans. Instrum. Meas.75(2026) 1–19, Art. no....

    2026

  14. [22]

    Alves Batistaet al.[GRAND Collaboration],GRANDlib: A simulation pipeline for the Giant Radio Array for Neutrino Detection (GRAND), Comput

    R. Alves Batistaet al.[GRAND Collaboration],GRANDlib: A simulation pipeline for the Giant Radio Array for Neutrino Detection (GRAND), Comput. Phys. Commun.308(2025) 109461, doi:10.1016/j.cpc.2024.109461

    2025

  15. [23]

    Ostapchenko,Monte Carlo treatment of hadronic interactions in enhanced Pomeron scheme: I

    S. Ostapchenko,Monte Carlo treatment of hadronic interactions in enhanced Pomeron scheme: I. QGSJET-II model, Phys. Rev. D83(2011) 014018, doi:10.1103/PhysRevD.83.014018

    2011 doi

  16. [24]

    Abreuet al.[Pierre Auger Collaboration],Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS), Astropart

    P. Abreuet al.[Pierre Auger Collaboration],Description of atmospheric conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS), Astropart. Phys.35(2012) 591–607, doi:10.1016/j.astropartphys.2011.12.002

    2012 doi

  17. [25]

    J. L. Kelley for the Pierre Auger Collaboration,Data acquisition, triggering, and filtering at the Auger Engineering Radio Array, Nucl. Instrum. Meth. A725(2013) 133–136, doi:10.1016/j.nima.2012.11.153

    2013 doi

  18. [26]

    Charrieret al.,Autonomous radio detection of air showers with the TREND50 antenna array, Astropart

    D. Charrieret al.,Autonomous radio detection of air showers with the TREND50 antenna array, Astropart. Phys.110(2019) 15–29, doi:10.1016/j.astropartphys.2019.03.002

    2019 doi

  19. [27]

    W. D. Apelet al.[LOPES Collaboration],The wavefront of the radio signal emitted by cosmic ray air showers, JCAP09(2014) 025, doi:10.1088/1475-7516/2014/09/025

    2014 doi

  20. [28]

    Decoene, O

    V. Decoene, O. Martineau-Huynh and M. Tueros,Radio wavefront of very inclined extensive air-showers: A simulation study for extended and sparse radio arrays, Astropart. Phys.145 (2023) 102779, doi:10.1016/j.astropartphys.2022.102779

    2023

  21. [29]

    Schlüter, M

    F. Schlüter, M. Gottowik, T. Huege and J. Rautenberg,Refractive displacement of the radio-emission footprint of inclined air showers simulated with CoREAS, Eur. Phys. J. C80 (2020) 643, doi:10.1140/epjc/s10052-020-8216-z

    2020 doi

  22. [30]

    A. Aabet al.[Pierre Auger Collaboration],Observation of inclined EeV air showers with the – 23 – radio detector of the Pierre Auger Observatory, JCAP10(2018) 026, doi:10.1088/1475-7516/2018/10/026

    2018 doi

  23. [31]

    Schlüter and T

    F. Schlüter and T. Huege,Signal model and event reconstruction for the radio detection of inclined air showers, JCAP01(2023) 008, doi:10.1088/1475-7516/2023/01/008

    2023 doi

  24. [32]

    Gülzow, R

    L. Gülzow, R. Engel, T. Huege, M. Roth and F. Schlüter,Signal model and energy reconstruction for the radio detection of inclined air showers in the 50–200 MHz frequency band, PoS(ICRC2025)283, arXiv:2507.06698 [astro-ph.IM] (2025). – 24 –

    2025 arXiv

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.