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REVIEW 4 major objections 3 minor 40 references

Gamma Neutron Radioactive Source Identification in Water Cherenkov Detectors

T0 review · 4 major / 3 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper claims that a single-photomultiplier water Cherenkov detector can separate neutron and gamma signals by combining a 3-sigma energy threshold with a soft-voting machine-learning ensemble.

desk verdict Careful experimental work, but the gamma/neutron labeling is likely circular and the ML classifier appears to separate two gamma populations, not neutrons from gammas. read the letter →

arxiv 2608.06518 v1 pith:YZINUBEQ submitted 2026-08-06 physics.ins-det hep-ex

A. Núñez Selin , C. Sarmiento Cano , H. Asorey , I. Sidelnik This is my paper · ORCID
classification physics.ins-dethep-ex
keywords WaterCherenkovdetectorgamma-neutrondiscriminationpulseshapeanalysismachinelearningensemblenuclearsecurityenergycalibration3-sigmacutoffamericium-berylliumsource
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 claims that a standard water Cherenkov detector can tell neutron-producing sources from pure gamma sources without helium-3 counters. It proposes a two-stage method: first, a statistical 3-sigma cutoff in the charge spectrum yields an energy calibration that screens low-energy gamma emitters; second, a soft-voting machine-learning ensemble trained on 32-bin pulse waveforms classifies the remaining sources. The ensemble reaches an accuracy of 0.816 and an ROC AUC of 0.921 on held-out test events, and it separates an unshielded mixed americium-beryllium field into neutron and gamma components. If the claim holds, water tanks already built for cosmic-ray physics could double as cheap, safe radiation monitors for nuclear security.

What carries the argument

The central mechanism is the cutoff point (CP), the first charge-spectrum bin where the source becomes statistically indistinguishable from background, $|N_{\rm src}-N_{\rm bkg}|<3\sigma$ with $\sigma=\sqrt{N_{\rm src}+N_{\rm bkg}}$. The CP both anchors the linear ADU-to-MeV calibration and defines a 'traffic-light' threshold: red below the neutron cutoff (pure gamma), green at the cutoff (neutron presence confirmed), yellow above it (high-energy gamma, neutrons unproven). The second mechanism is the soft-voting ensemble, which combines a Bagging classifier, CatBoost, and a multilayer perceptron over the 32 time bins of each PMT pulse; per-model weights are set proportional to validation F1 scores, and the decision threshold is tuned to 0.52 to balance false positives and false negatives. The ensemble operates inside a 4500–8000 ADU charge window chosen so the neutron signal dominates background.

What would settle it

Run the trained ensemble on pulses from a neutron-only source that produces capture gammas inside the water at energies different from 2.22 MeV, with no 4.44 MeV gamma present; if accuracy falls to chance, the discrimination is energy-based rather than neutron-based.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a single-photomultiplier water Cherenkov detector carries enough pulse-shape information to separate neutron-induced from gamma-induced signals once the energy axis is calibrated. The 3-$\sigma$ cutoff procedure anchors a linear charge-to-energy relation, $E = (4.00\pm0.19)\times10^{-4} Q + 0.26$ (MeV, ADU), and places a neutron threshold near 9000 ADU, so sources ending below it are identifiable as pure gamma emitters. Above that threshold, a soft-voting ensemble of three classifiers—a Bagging classifier, CatBoost, and a multilayer perceptron—trained on 32 time bins of each pulse achieves accuracy $0.816$ and ROC AUC $0.921$ at decision threshold $0.52$, with balanced true-positive and true-negative rates. Applied to unshielded $^{241}$AmBe pulses never used in training, the model separates the mixed field into neutron and gamma spectral components consistent with the shielded calibrations. The authors conclude that this two-stage architecture gives water-based detectors an operational radiation-identification capability.

Load-bearing premise

The load-bearing premise is that pulses recorded behind lead shielding can be labeled neutron signals and pulses behind cadmium-lined borated paraffin can be labeled gamma signals, so that the pulse shapes learned by the classifier generalize to an unknown mixed radiation field.

Editorial extensions

If this is right

  • A source whose 3-sigma cutoff falls below the neutron threshold (~9000 ADU, about 2 MeV) can be screened as pure gamma without pulse-level analysis.
  • For sources above the threshold, the trained ensemble can decompose a mixed field into neutron and gamma components, as demonstrated on unshielded 241AmBe pulses not used in training.
  • Water Cherenkov detectors could serve as scalable, helium-free radiation monitors for nuclear security, using the same tanks as cosmic-ray observatories.
  • The method is detector-specific: any change in PMT gain, geometry, or electronics shifts the calibration, so the paper requires recalibration and possible classifier retraining before field deployment.
  • Longer acquisition times improve the reliability of the 3-sigma cutoff, so weak or distant sources need longer measurements than the 5-minute lab runs.

Reading between the lines

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

  • Editorial inference: because the WCD detects neutrons only through the 2.22 MeV capture gamma, the machine-learning model may actually be separating 2.22 MeV capture-gamma pulses from 4.44 MeV source-gamma pulses; the paper does not test transfer to other neutron energies or geometries.
  • Editorial inference: the cutoff-based calibration could work as a self-calibration tool for other water Cherenkov installations, using any mixed source with a known high-energy gamma endpoint as the anchor.
  • Editorial inference: a direct test of particle-based discrimination would be to train on one neutron source and test on a different neutron source (different energy or moderation geometry); maintaining high AUC would show the features are neutron-specific.
  • Editorial inference: with ensemble weights all near one-third and similar F1 scores, a simple majority vote of the three classifiers might match the soft-voting performance, which would simplify field deployment; the paper does not report this comparison.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 3 minor

Summary. The paper claims to demonstrate gamma-neutron discrimination in a water Cherenkov detector using a two-stage method: a 3-sigma cutoff to establish an energy threshold and a soft-voting machine-learning ensemble to classify pulses. The experimental data come from a 241AmBe source under lead, borated-paraffin/cadmium, lead/paraffin, and unshielded configurations, with 60Co and 137Cs used for calibration. The authors report a linear ADU-to-MeV calibration, a physically grounded neutron threshold, and an ML ensemble with accuracy 0.816 and ROC-AUC 0.921, and they apply the trained model to decompose the unshielded mixed field into neutron and gamma components.

Significance. If the claims were valid, the work would provide a practical, 3He-free route to neutron-gamma discrimination in water Cherenkov detectors, relevant to nuclear security and radiation monitoring. The paper contains a detailed experimental description, transparent uncertainty propagation for the cutoff determination, and a reproducible ML pipeline with explicit hyperparameters and cross-validation. These are concrete strengths. However, the central claim is not established because the training labels do not correspond to the physical particle classes, and the reported ML performance is affected by test-set leakage. The significance of the work as a demonstration of gamma-neutron discrimination is therefore much lower than claimed.

major comments (4)
  1. [Sections 2.2, 5.2, Eq. (3)] The training labels for the ML classifier are not physically valid. The paper states in Section 2.2 that neutrons in a WCD are detected only through the 2.22 MeV gamma from hydrogen capture. Using the calibration in Eq. (3), the selected charge window 4500-8000 ADU corresponds to 2.06-3.46 MeV. A 2.22 MeV gamma can deposit at most 2.22 MeV (approximately 4900 ADU), so any pulse in the window above that energy cannot be a neutron-capture event; those pulses must originate from residual 4.44 MeV source gammas that survive the lead shield, as acknowledged in Section 2.4. Thus the Pb-shielded 'neutron' training class contains a substantial fraction of non-neutron events, and the classifier may be separating gamma populations with different shielding/attenuation histories rather than neutron pulses from gamma pulses.
  2. [Section 5.4, Fig. 9] The reported accuracy is optimistically biased by threshold selection on the test set. The text says the ensemble was evaluated on the test set across a range of decision thresholds and the optimal threshold (0.52) was selected at the intersection of TP and TN rates; the same test set is then used to produce the confusion matrix and the reported accuracy of 0.816. This is a form of test-set leakage. The threshold should be tuned on a validation fold or with nested cross-validation, and the final metrics should be reported on a held-out test set that is never used for threshold or model selection. The ROC-AUC may also be inflated if the threshold is not the only quantity tuned on the test set.
  3. [Table 1, Section 3.2] The energy calibration is internally inconsistent and does not support the claimed physical interpretation. Table 1 lists the Pb configuration cutoff as 9000±1300 ADU with an associated energy of 2.22 MeV, but Eq. (3) converts 9000 ADU to 3.86 MeV. Similarly, the Cd/B-Paraffin cutoff of 14900±3000 ADU is associated with 4.44 MeV, but Eq. (3) gives 6.22 MeV. Since a single gamma cannot deposit more than its full energy, these cutoffs cannot represent the maximum energy deposition of the stated gamma lines. The linear fit in Eq. (3) is therefore not a valid mapping between charge and deposited energy in the region used for the threshold and the ML window. The acknowledgement in Section 3.2 that the reduced chi-square exceeds unity does not resolve this systematic inconsistency.
  4. [Section 5.3, Fig. 5] The unshielded 241AmBe dataset is described as an independent test, but it has no ground-truth labels for individual pulses. The 'predicted neutron and gamma components' in Fig. 5 are simply the model's assignments under the learned shielding-dependent classes; they cannot validate the classifier's ability to identify neutrons in a mixed field. To demonstrate feasibility for mixed radiation fields, the authors would need tagged events (for example, time correlation with the AmBe alpha particle or a well-characterized neutron source such as 252Cf) or a validated Monte Carlo simulation with known particle identities.
minor comments (3)
  1. [Abstract] The abstract states 'achieving an accuracy of 0.816 and an area under the Receiver Operating Characteristic (ROC) curve' but omits the numerical AUC value (0.921, reported later). The AUC should be stated in the abstract for completeness.
  2. [Section 5.2] The text says the classifier is most effective in the mid energy region (1.5-2.5 MeV), but the selected charge window 4500-8000 ADU corresponds to 2.06-3.46 MeV via Eq. (3). The stated energy range is inconsistent with the window; this should be reconciled or corrected.
  3. [Section 5.3] The accuracy threshold of 0.75 used to select 12 classifiers should state explicitly whether this selection is based on validation-fold performance or test-set performance. If it is based on the test set, it introduces additional selection bias beyond the threshold tuning discussed above.

Circularity Check

2 steps flagged · score 6.0 of 10

Test-set threshold fit and shielding-defined labels make the reported accuracy and the neutron/gamma decomposition partly forced by construction.

  1. fitted input called prediction [Section 5.4 (Voting Classifier), threshold optimization and accuracy reporting]
    "However, to ensure optimal classification performance for both neutron and gamma signals, it is necessary to fine-tune this 0.5 threshold. To determine the most suitable one, the trained ensemble was evaluated on the test set across a range of decision thresholds. The optimal threshold was selected at the point where TP and TN rates were balanced... After establishing the optimal threshold, the confusion matrix was generated... As seen in Fig. 9 right, the accuracy of the VotingClassifier model is ∼0.816."

    The reported test accuracy (0.816) and the confusion matrix are computed on the same test set that was used to select the 0.52 decision threshold. The threshold is therefore a parameter fitted to the test labels, and the accuracy is the in-sample score of that fitted decision rule, not a hold-out prediction. The abstract's claim of 'achieving an accuracy of 0.816' is thus partly forced by the fitting procedure: a threshold chosen to balance TP and TN on the test set will, by construction, score at least as well as any other threshold on that same set, so the number is not an unbiased estimate of generalization.

  2. self definitional [Section 2.2, Section 2.4, Section 5.1, Section 5.2, and calibration Eq. 3]
    "Neutron Radiation: Neutrons are moderated by water and primarily captured by hydrogen nuclei (1H), leading to the emission of 2.22 MeV gamma rays which then interact with the medium. ... we defined a specific charge range, limiting the spectrum to 4500–8000 ADUs. ... Our method focuses on distinguishing between the 4.44 MeV gamma emissions from the 241AmBe source, and neutron capture induced signals, all while maintaining a fixed deposited energy range within the detector."

    Using the paper's own calibration E(MeV)=(4.00e-4)Q+0.26, the 4500 ADU lower edge of the 'neutron' training window is 2.06 MeV, above the ~1.99 MeV maximum Compton-electron energy of the 2.22 MeV capture gamma that Section 2.2 identifies as the only neutron signature in a WCD. Neutron-capture events therefore cannot populate the Pb-shielded class in this window. Since Section 2.4 admits that 10 cm lead still leaves a residual 4.44 MeV gamma component, the pulses labeled 'neutron' are, by the paper's own physics, residual 4.44 MeV gamma events, while the 'gamma' class is the direct 4.44 MeV gamma. The ML classifier is thus separating two gamma populations distinguished by shielding and attenuation history, and the claimed 'gamma-neutron discrimination' plus the Fig.

full rationale

The paper does not rely on a load-bearing self-citation: the neutron-capture physics is standard and the ML experiment is self-contained in its own data. The circularity is methodological. First, the 0.52 decision threshold is selected on the test set and the accuracy/confusion matrix are then reported on that same set, so 0.816 is not a hold-out estimate. Second, and more fundamentally, applying the paper's own calibration to the 4500–8000 ADU window shows that 2.22 MeV capture-gamma events (the stated neutron signature) cannot appear in that window, so the Pb-shielded 'neutron' labels are dominated by residual 4.44 MeV gamma leakage. The model therefore learns to separate two shielding configurations of the same 4.44 MeV gamma, not neutron from gamma, and the Fig. 5 decomposition is a relabeling of the training classes. The ROC AUC of 0.921 retains some independent content because it is threshold-independent, but it is still computed on the same self-defined labels and data-selected window, so the central accuracy claim and the physical interpretation of the 'prediction' reduce by construction.

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

The central claims rest on a calibration fit with large systematic uncertainties, a surrogate labeling scheme for neutron versus gamma events, a post-hoc charge window, and a test-set-tuned threshold. No new physical entities are introduced. The most fragile ingredient is the physical interpretation of the neutron class, since it is a capture-gamma signal in a water Cherenkov detector.

free parameters (5)
  • Energy calibration slope m = (4.00 ± 0.19) x 10^-4 MeV/ADU
    Linear regression slope fitted to the six calibration points (known source gamma energies vs 3-sigma cutoff positions in ADU), reported in Eq. 3 and Figure 2.
  • 3-sigma cutoff criterion = sigma = 3
    The significance level used to define cutoff bins in Eq. 2; a conventional methodological choice that sets all energy thresholds.
  • Charge window for ML classification = 4500 to 8000 ADU
    Selected post hoc in Section 5.2 to 'maximize the neutron signal relative to background noise'; restricts the domain of the classifier and affects reported accuracy.
  • Ensemble decision threshold = 0.52
    Tuned on the test set in Section 5.4 to balance true positive and false positive rates; tuning on the test set can inflate the reported accuracy.
  • Soft-voting weights = 0.3318, 0.3342, 0.3340
    Derived from validation F1 scores of the three base classifiers (Bagging, CatBoost, MLP) as described in Section 5.4; effectively fitted parameters of the ensemble.
assumptions (5)
  • domain assumption Neutrons are detected exclusively through the 2.22 MeV gamma ray emitted after capture by hydrogen in water.
    Invoked in Section 2.2 and used to interpret the lead-shielded AmBe signal as a neutron signature. In a WCD the actual detected event is the capture gamma, not the neutron itself.
  • ad hoc to paper The 3-sigma cutoff bin corresponds to the maximum energy deposition fully contained in the detector.
    Stated in Section 3.1 with no direct verification that the cutoff is the full-energy endpoint rather than the Compton edge; the energy calibration depends on this identification.
  • domain assumption The shielding configurations produce sufficiently pure label classes (neutron vs gamma) for supervised learning.
    Sections 2.4 and 5.3 label Pb-shielded pulses as neutrons and Cd/B-Paraffin pulses as gammas; residual gamma leakage and the gamma nature of the neutron signal are acknowledged but not quantified.
  • ad hoc to paper Pulse shape differences learned from the two shielding conditions generalize to arbitrary mixed radiation fields.
    The independent test on the unshielded source lacks ground truth; the paper assumes that the learned separation transfers, as shown in Figure 5 and Section 5.4.
  • standard math Standard data-splitting, class-balancing, and feature-scaling procedures are correctly applied.
    Section 5.3 describes the pipeline; we assume the implementation follows standard practice, but no code is provided to verify it.

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Cite this review

Pith. "Pith review of Gamma Neutron Radioactive Source Identification in Water Cherenkov Detectors." pith.science (2026). https://pith.science/paper/YZINUBEQ

@misc{pith2026260806518,
  author       = {Pith},
  title        = {Pith review of: Gamma Neutron Radioactive Source Identification in Water Cherenkov Detectors},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YZINUBEQ}},
  note         = {Machine review of arXiv:2608.06518}
}
abstract

Water Cherenkov Detectors (WCDs) are a robust technology widely used in astrophysics, high energy physics, and recently nuclear security applications. They detect high energy interactions through the Cherenkov light emitted by charged particles traveling faster than the speed of light in water. In this work, we demonstrate the feasibility of gamma-neutron discrimination in WCDs using a combined methodology that integrates statistical analysis with machine learning techniques. The experimental setup employs different shielding configurations to isolate gamma and neutron contributions from a \textsuperscript{241}AmBe source, while \textsuperscript{60}Co and \textsuperscript{137}Cs sources are used to establish a signal to energy calibration. A statistical analysis based on a $3\sigma$ significance criterion is used to define energy thresholds, enabling a linear relationship between the measured charge spectrum and the deposited energy. Building on this calibration, pulse shape information is further exploited through machine learning methods to improve event classification. An ensemble model based on a soft-voting strategy combining a Bagging classifier, CatBoost, and a Multilayer Perceptron was trained on detector signals acquired under different shielding conditions, achieving an accuracy of 0.816 and an area under the Receiver Operating Characteristic (ROC) curve. The combined approach demonstrates that statistical thresholding provides a physically grounded discrimination baseline across the full energy range, while machine learning enhances classification performance at higher energies by leveraging pulse level information. This integrated strategy improves radiation identification capabilities in water Cherenkov detectors, with potential applications in nuclear security and radiation detection.

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Reference graph

Works this paper leans on

40 extracted references · 31 canonical work pages

  1. [32]

    Alejandro N´ u˜ nez Selin, Iv´ an Sidelnik, Christian Sarmiento Cano, Hern´ an Asorey, and Luis Nu˜ nez. Two stage gamma–neutron source classification in water cherenkov detectors: Energy threshold screening and machine learning pulse analysis.Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associa...

    work page 2026

  2. [1]

    Abraham and et

    J. Abraham and et. al. The pierre auger cosmic ray observatory.Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrom- eters, Detectors and Associated Equipment, 798:172–213, 2015. Available at: https://doi.org/10.1016/j.nima.2015.06.058, Accessed: 2025-01-14

    doi:10.1016/j.nima.2015.06.058 2015

  3. [2]

    Springer Science & Business Media, 2013

    Karl-Heinrich Beckurts and Karl Wirtz.Neutron Physics. Springer Science & Business Media, 2013

    work page 2013

  4. [3]

    Jaime Betancourt, Christian Sarmiento Cano, Alejandro Nu˜ nez Selin, Iv´ an Sidel- nik, Hern´ an Asorey, Yessica Dominguez Ballesteros, and Luis Nu˜ nez. Enhanced water cherenkov detector for soil moisture detection.Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2025

    work page 2025

  5. [4]

    Bevington and D

    Philip R. Bevington and D. Keith Robinson.Data Reduction and Error Analysis for the Physical Sciences. McGraw-Hill, 2003

    work page 2003

  6. [5]

    Springer, 2006

    Christopher M Bishop and Nasser M Nasrabadi.Pattern recognition and machine learning, volume 4. Springer, 2006

    2006

  7. [6]

    Bagging predictors.Machine learning, 24:123–140, 1996

    Leo Breiman. Bagging predictors.Machine learning, 24:123–140, 1996

    1996

  8. [7]

    Random forests.Machine learning, 45:5–32, 2001

    Leo Breiman. Random forests.Machine learning, 45:5–32, 2001

    2001

Show all 40 references
  1. [8]

    Xgboost: A scalable tree boosting system

    Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. InProceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pages 785–794, 2016

    2016

  2. [9]

    Visible light from clear liquids under the action of gamma radiation.Comptes Rendus (Doklady) de l’Aeademie des Sciences de l’URSS, 2(8):451–454, 1934

    Pavel A Cherenkov. Visible light from clear liquids under the action of gamma radiation.Comptes Rendus (Doklady) de l’Aeademie des Sciences de l’URSS, 2(8):451–454, 1934

    1934

  3. [10]

    DL Chichester and BW Blackburn. Radiation fields from neutron generators shielded with different materials.Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 261(1-2):845– 849, 2007

    2007

  4. [11]

    Lago ecosystem git repository

    The LAGO Collaboration. Lago ecosystem git repository. Available at: https://github.com/lagoprojectrp/lago ecosystem/, Accessed: 2025-01-14

    2025

  5. [12]

    The lago project, 2019

    The LAGO Collaboration. The lago project, 2019

    2019

  6. [13]

    Support-vector networks.Machine learning, 20:273–297, 1995

    Corinna Cortes and Vladimir Vapnik. Support-vector networks.Machine learning, 20:273–297, 1995

    1995

  7. [14]

    Number 1 in 2

    Anthony Christopher Davison and David Victor Hinkley.Bootstrap methods and their application. Number 1 in 2. Cambridge university press, 1997

    1997

  8. [15]

    Statistical methods in experimental physics, 1973

    William Templeton Eadie and Larry D Kirkpatrick. Statistical methods in experimental physics, 1973

    1973

  9. [16]

    Filevich, P

    A. Filevich, P. Bauleo, H. Bianchi, J. R. Martino, and G. Torlasco. Spectral- directional reflectivity of tyvek immersed in water.Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 423(1):108–118, 199...

    1999 doi

  10. [17]

    Coherent visible radiation of fast electrons passing through matter

    IETIM Frank and Ig Tamm. Coherent visible radiation of fast electrons passing through matter. InSelected Papers, pages 29–35. Springer, 1991

    1991

  11. [18]

    S Fukuda, Y Fukuda, Takashi Hayakawa, E Ichihara, Masaki Ishitsuka, Y Itow, 21 T Kajita, J Kameda, K Kaneyuki, Shunsuke Kasuga, et al. The super-kamiokande detector.Nuclear Instruments and Methods in Physics Research Section A: Accel- erators, Spectrometers, Detectors and Asso...

    2003

  12. [19]

    Neutron spectrum of an am241-be (α, n) source

    KW Geiger and CK Hargrove. Neutron spectrum of an am241-be (α, n) source. Nuclear Physics, 53:204–208, 1964

    1964

  13. [20]

    MIT press Cambridge, 2016

    Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio.Deep learning, volume 1. MIT press Cambridge, 2016

    2016

  14. [21]

    Springer, 2009

    Trevor Hastie, Robert Tibshirani, Jerome H Friedman, and Jerome H Fried- man.The elements of statistical learning: data mining, inference, and prediction, volume 2. Springer, 2009

    2009

  15. [22]

    Using machine learn- ing to improve neutron identification in water cherenkov detectors.Frontiers in big Data, 5:978857, 2022

    Blair Jamieson, Matt Stubbs, Sheela Ramanna, John Walker, Nick Prouse, Ryosuke Akutsu, Patrick de Perio, and Wojciech Fedorko. Using machine learn- ing to improve neutron identification in water cherenkov detectors.Frontiers in big Data, 5:978857, 2022

    2022

  16. [23]

    Radiation detection and measurement.John & Wiley Sons Inc, 2010

    Glenn F Knoll. Radiation detection and measurement.John & Wiley Sons Inc, 2010

    2010

  17. [24]

    A study of cross-validation and bootstrap for accuracy estima- tion and model selection

    Ron Kohavi et al. A study of cross-validation and bootstrap for accuracy estima- tion and model selection. InIjcai, volume 14 of3, pages 1137–1145. Montreal, Canada, 1995

    1995

  18. [25]

    John Wiley & Sons, 2014

    Ludmila I Kuncheva.Combining pattern classifiers: methods and algorithms. John Wiley & Sons, 2014

    2014

  19. [26]

    Imbalanced- learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning.Journal of machine learning research, 18(17):1–5, 2017

    Guillaume Lema ˜AˇZtre, Fernando Nogueira, and Christos K Aridas. Imbalanced- learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning.Journal of machine learning research, 18(17):1–5, 2017

    2017

  20. [27]

    Otiniano, A

    L. Otiniano, A. Taboada, H. Asorey, I. Sidelnik, C. Castromonte, and A. Fauth. Measurement of the muon lifetime and the michel spectrum in the lago water cherenkov detectors as a tool to enhance the signal-to-noise ratio.Nuclear Instru- ments and Methods in Physics Research Se...

    2023

  21. [28]

    Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011

    Fabian Pedregosa, Ga¨ el Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830, 2011

    2011

  22. [29]

    Catboost: unbiased boosting with categorical features

    Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Doro- gush, and Andrey Gulin. Catboost: unbiased boosting with categorical features. Advances in neural information processing systems, 31, 2018

    2018

  23. [30]

    Sachetti, N

    F. Sachetti, N. Colonna, R. Faccini, B. Guerard, R. Hall-Wilton, F. Murtas, C. Petrillo, A. Pietropaolo, N. Rhodes, L. Quintieri, M. Tardocchi, and P. Valente. 3he-free neutron detector and their application. Technical report, The European Physical Journal Plus, 2015

    2015

  24. [31]

    N´ u˜ nez, Sergio Dasso, Iv´ an Sidelnik, and Hern´ an Asorey

    Christian Sarmiento-Cano, Mauricio Su´ arez-Dur´ an, Rolando Calder´ on-Ardila, Adriana V´ asquez-Ram ´ ırez, Andrei Jaimes-Motta, Luis A. N´ u˜ nez, Sergio Dasso, Iv´ an Sidelnik, and Hern´ an Asorey. The ARTI framework: Cosmic rays atmo- spheric background simulations.The Eu...

    2022

  25. [33]

    Sidelnik, H

    I. Sidelnik, H. Asorey, J. J. Blostein, and M. G´ omez Berisso. Neutron detection using a water cherenkov detector with pure water and a single pmt.Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrom- eters, Detectors and Associated Equipment,...

    2017 doi

  26. [34]

    Sidelnik, H

    I. Sidelnik, H. Asorey, N. Guarin, M. S. Dur´ an, F. A. Bessia, L. H. Arnaldi, M. G´ omez Berisso, J. Lipovetzky, M. P´ erez, M. Sofo Haro, and J. J. Blostein. Neutron detection capabilities of water cherenkov detectors.Nuclear Instruments and Methods in Physics Research Secti...

    2018

  27. [35]

    Iv´ an Sidelnik, Hernan Asorey, Nicol´ as Guarin, Mauricio Suar´ ez Dur´ an, Jos´ e Lipovetzky, Luis Horacio Arnaldi, Mart ´ ın P´ erez, Miguel Sofo Haro, Mar- iano G´ omez Berisso, Fabricio Alcalde Bessia, et al. Enhancing neu- tron detection capabilities of a water cherenkov...

    2020

  28. [36]

    Pitaya stemlab-125-14 documentation

    RedPitaya STEMlab. Pitaya stemlab-125-14 documentation. Available at: https://redpitaya.com/stemlab-125-14/, Accessed: 2025-01-14

    2025

  29. [37]

    Comprehensive machine learning model comparison for cherenkov and scintillation light separation due to particle interactions.arXiv preprint arXiv:2406.09191, 2024

    Emrah Tiras, Merve Tas, Dilara Kizilkaya, Muhammet Anil Yagiz, and Mustafa Kandemir. Comprehensive machine learning model comparison for cherenkov and scintillation light separation due to particle interactions.arXiv preprint arXiv:2406.09191, 2024

    2024 arXiv

  30. [38]

    Enhanced particle classifica- tion in water cherenkov detectors using machine learning: Modeling and validation with monte carlo simulation datasets.Atmosphere, 15(9):1039, 2024

    Ticiano Jorge Torres Peralta, Maria Graciela Molina, Hernan Asorey, Ivan Sidel- nik, Antonio Juan Rubio-Montero, Sergio Dasso, Rafael Mayo-Garcia, Alvaro Taboada, Luis Otiniano, and LAGO Collaboration. Enhanced particle classifica- tion in water cherenkov detectors using machi...

    2024

  31. [39]

    Watanabe, H

    H. Watanabe, H. Zhang, K. Abe, and et al. First study of neutron tagging with a water cherenkov detector.Astroparticle Physics, 31(4):320–328, 2009. Available at: https://doi.org/10.1016/j.astropartphys.2009.03.002, Accessed: 2025-01-14

    2009 doi

  32. [40]

    Oxford University Press, 2017

    Anthony G Wright.The Photomultiplier Handbook. Oxford University Press, 2017. 23

    2017

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.