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REVIEW 4 major objections 6 minor 25 references

Universal Convergence Metric for Time-Resolved Neutron Scattering

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A dimensionless metric predicts when time-resolved neutron scattering data are sufficient, with a universal power-law exponent between -2 and -1.

desk verdict A practically motivated stopping-rule metric for time-resolved SANS, but the universal power-law claim rests on a mistaken variance calculation and a per-system fit that forces the collapse. read the letter →

arxiv 2505.13512 v1 pith:7YLYTGOM submitted 2025-05-17 physics.ins-det physics.app-phphysics.data-an

Chi-Huan Tung , Lijie Ding , Yuya Shinohara , Guan-Rong Huang , Jan-Michael Carrillo , Wei-Ren Chen , Changwoo Do This is my paper · ORCID
classification physics.ins-detphysics.app-phphysics.data-an
keywords time-resolvedSANSGaussianprocessregressionmeasurementsufficiencydimensionlessconvergencemetricuniversalpower-lawscalingearly-timeforecastingPoissonstatisticscentrallimittheorem
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

This paper claims that the time evolution of a small-angle neutron scattering profile encodes its own stopping rule. By comparing each time frame with the previous frame through a dimensionless quantity that divides the change in intensity by the corresponding uncertainty and average signal, the method defines a metric that needs no model of the sample and no knowledge of the final profile. Across several soft-matter systems the metric collapses onto a single power-law curve once time is rescaled by a system-specific characteristic time $t^{\star}$, with an exponent between $-2$ and $-1$. The trend stabilizes within the first ten time steps, so the metric can forecast when a measurement will be statistically sufficient and can terminate data collection early. If correct, this would make low-flux neutron and X-ray scattering experiments considerably more efficient.

What carries the argument

The load-bearing object is the dimensionless convergence metric of Eq. (2): the $Q$-average of the squared change in inferred intensity between successive time frames, divided by the uncertainty ratio $\Delta I(t_i)/\sqrt{\langle[\Delta I(t_i)]^2\rangle_Q}$ and by the average squared intensity $\langle[I(t_i)]^2\rangle_Q$. This quantity requires no ground truth and can be computed online during an experiment. Its scaling behavior is governed by two mechanisms: the zero-count error floor that makes the short-time metric decay as $t^{-2}$, and the Poisson/central-limit behavior of a normal counting regime that gives a $t^{-1}$ decay; the observed exponent therefore lies between $-2$ and $-1$. A Gaussian-process regression kernel supplies the denoised profiles whose faster convergence enables early-time forecasting.

What would settle it

Reanalyze one of the paper's time-resolved datasets using an alternative zero-count treatment, for example discarding empty bins or assigning them a fractional uncertainty, and recompute the dimensionless metric. If the early-time slope moves away from $-2$ or the rescaled curves for different systems stop collapsing, the universal range is an artifact of that convention. The same test can be run on synthetic Poisson data where the ground truth is known.

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Extended reading notes

Core claim

The central discovery is that the $Q$-averaged mean relative variation between successive scattering profiles decays as a universal power law in measurement time. The paper defines the variation as the squared difference between intensity profiles at consecutive time steps, normalized by the experimental uncertainty and by the average squared intensity, so that the quantity is dimensionless and computable from the data alone. When time is divided by a system-specific characteristic time $t^{\star}$ (the time at which the raw-data variation equals unity), the curves for different systems collapse onto a single trend with an exponent between $-2$ and $-1$. The exponent is derived from two statistical regimes: at short times a zero-count error floor gives $\Delta I \propto 1/t$ and hence a $t^{-2}$ decay, while at long times Poisson/central-limit statistics give $\Delta I \propto 1/\sqrt{t}$ and a $t^{-1}$ decay. The GPR-denoised version of the same metric reaches statistical stability about an order of magnitude earlier than the raw data, making early forecasting practical.

Load-bearing premise

The load-bearing premise is the data-reduction convention that detector bins registering zero neutrons are assigned an uncertainty of one; if a different zero-count treatment is used, the short-time $t^{-2}$ branch, and with it the claimed universal exponent range between $-2$ and $-1$, would no longer follow.

Editorial extensions

If this is right

  • An experimenter can decide within the first ten time steps how much longer to collect data, terminating the measurement once the forecast variation falls below a chosen threshold.
  • Low-flux instruments, including compact accelerator-based neutron sources, could obtain statistically sufficient profiles in a fraction of the usual beam time.
  • Because the metric is dimensionless and model-free, it transfers directly to any SANS instrument with a two-dimensional detector, and the paper argues it extends to laboratory SAXS and X-ray scattering.
  • GPR-denoised profiles reach convergence roughly ten times faster than raw profiles, widening the time window in which kinetic processes can be resolved.
  • The same early-time regression can provide forecast confidence intervals for the required measurement duration, supporting adaptive real-time experimental control.

Reading between the lines

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

  • The fitted exponent can serve as a live regime diagnostic: a slope near $-2$ means the experiment is still dominated by the zero-count error floor, while a slope drifting toward $-1$ indicates enough counts for normal Poisson averaging.
  • The same adjacent-frame comparison should apply to any time-binned counting experiment, such as X-ray photon correlation, dynamic light scattering, or electron microscopy frame stacks, provided the zero-count error convention is handled explicitly; this is a testable generalization the paper does not develop.
  • If the collapse is truly universal, defining $t^{\star}$ from the long-time asymptotic behavior instead of from the variation-equals-unity point should yield the same master curve; checking this would directly test whether the normalization is arbitrary.
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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 / 6 minor

Summary. The manuscript proposes a dimensionless metric, Eqs. (1)-(2), for judging when a time-resolved SANS measurement has converged, based on successive cumulative intensity profiles processed with Gaussian Process Regression. The authors apply the metric to EQSANS data from CTAB/NaSal and several other soft-matter systems, observe an approximately linear log-log decrease of the metric with exposure time, and claim that after rescaling time by a system-specific characteristic time t* the data collapse onto a universal power law with exponent between -2 and -1. They further claim that linear fits using only the first ten time steps are stable enough to forecast the required measurement duration.

Significance. The practical goal is valuable: a data-driven stopping rule for neutron scattering experiments, especially at low-flux facilities, would improve beam utilization, and the integration of GPR into the EQSANS workflow is a useful engineering contribution. The paper also tests the metric on several experimental datasets and demonstrates a forecasting protocol in Fig. 4. However, the two headline claims—the derivation of the t^{-2} short-time scaling and the universality of the collapse—are not supported as presented. The derivation mis-treats cumulative profiles as independent, and the collapse is produced by a per-system normalization that pins all curves to a common point by construction. These issues are central, not cosmetic.

major comments (4)
  1. [II, Eq. (4)] The derivation after Eq. (3) models I(t_i) and I(t_{i-1}) as independent random variables and writes Var[I(t_i)-I(t_{i-1})]=Var[I(t_i)]+Var[I(t_{i-1})]. This is inconsistent with the definition of I(t) as the cumulative intensity profile. If N(t) is a Poisson process with mean λt and I(t)=N(t)/t, then Cov[N(t_i),N(t_{i-1})]=Var[N(t_{i-1})]=λt_{i-1}, and the correct variance is Var[I(t_i)-I(t_{i-1})]=λ(1/t_{i-1}-1/t_i), not λ(1/t_i^2+1/t_{i-1}^2). Consequently the proportionality leading to Eq. (6) does not follow, and the predicted t^{-2} short-time scaling is not established. The manuscript must either use the correct covariance or explicitly redefine I(t_i) as independent repeated measurements, which would contradict the cumulative-exposure description in the text and Fig. 1.
  2. [II, paragraph before Fig. 3(a)] The universal collapse in Fig. 3(a) is enforced by construction. The text states that t* is defined so that the mean relative variation of the raw data equals 1 at t=t*; rescaling each system by its own t* forces all raw-data curves through the point (1,1). The overlap at the reference point therefore cannot be used as evidence for a universal curve. What remains testable is the slope of each curve and whether the slopes agree. The authors should report per-system slopes with uncertainties and test the collapse after removing the trivial pinning, for example by fixing t* from an independent physical timescale or by comparing residuals of the fitted slopes.
  3. [II, Eqs. (6)-(8)] The claimed exponent range (-2,-1) is derived from the model, not discovered from the data. Because the short-time end of the derivation rests on the flawed independence assumption (Major Comment 1) and on the instrument-specific zero-count floor (the assignment of unit uncertainty to empty bins), the statement that 'the extracted scaling exponents cluster around -2' has no valid theoretical grounding. If the authors wish to retain the universal-exponent claim, they need a corrected derivation that accounts for the cumulative structure of the profiles. If such a derivation is not possible, the paper should be reframed as reporting an empirical trend rather than a universal law.
  4. [II, paragraph after Eq. (3)] The zero-count bin convention ΔI ∝ 1/t is load-bearing: it is the sole source of the t^{-2} branch. The authors should test whether the claimed scaling and collapse survive alternative error assignments, for example a floor of 0.5 or 2, or a proper Poisson treatment of zero counts. Without such a sensitivity test, the universality claim depends on a particular data-reduction choice rather than on the physics of the measurement.
minor comments (6)
  1. [II, paragraph after Eq. (3)] The phrase 'this behavior has a well-defined statistical interpretation' begins with a lowercase letter, and the following sentence is a run-on; also, the claimed scaling of the l-th cumulant as O(n^{1-l}) is not defined. Please define n and l and connect that statement to the rest of the argument.
  2. [II, Eq. (2)] The notation with angle brackets subscripted by Q after the squared term, together with the separate factor 1/\langle[I(t_i)]^2\rangle_Q, is easy to misread; use explicit brackets, e.g. \left\langle [\cdots]^2\right\rangle_Q.
  3. [Fig. 3(b)] The text says 'the solid line representing the overall best-fit trend' but the figure caption refers to 'vertical lines'; make the description consistent and add error bars or confidence intervals to the individual exponents.
  4. [Fig. 4] The statement that forecast uncertainty 'falls below a level of approximately 2' is unexplained; specify what quantity is plotted and how the threshold is chosen.
  5. [II] The GPR kernel and its hyperparameters are not described here. Since the metric is computed from GPR-inferred profiles, provide the implementation details or explicitly state that they are fully given in Ref. 9.
  6. [II, paragraph after Eq. (8)] Typo: 'Thist−1 scaling' should be 'This t^{-1} scaling'.

Circularity Check

1 steps flagged · score 4.0 of 10

The universal collapse is partly constructed by the t* normalization, but the fitted power-law exponent retains independent empirical content.

  1. self definitional [Section II, definition of t* immediately before Fig. 3(a)]
    "the measurement time is rescaled by a system-specific characteristic time t*, defined such that the mean relative variation for the raw data equals 1 at t=t*. This normalization aligns the data from different systems at a common statistical reference point"

    By definition, every system's raw-data metric satisfies M_raw(t*)=1, so every curve is forced through the point (t/t*=1, value=1) in the normalized plot. That shared crossing point in Fig. 3(a) is therefore guaranteed by construction and cannot be used as independent evidence of universality. The genuinely empirical content is the fitted power-law slope, which is not fixed by the t* choice; the abstract's claim that the variation 'collapses onto a single curve' conflates this constructed intersection with the independently fitted slope alignment.

full rationale

The central claim of a universal power-law exponent is not a pure tautology: the per-system slopes in Fig. 3(b) are obtained by linear regression of the metric versus time, and the t* normalization only shifts each curve horizontally so that all curves share one reference point. That reference-point overlap is self-definitional, but the slope clustering near -2 is an independent empirical observation, albeit one that is also derived analytically from the zero-count error-floor convention. The GPR framework is imported from the authors' prior work (Ref. 9), but it is used as a denoising tool rather than as the basis of the convergence metric, so that self-citation is not load-bearing. A separate statistical concern, noted in the skeptic reading, is that Eq. (4) treats cumulative profiles I(t_i) and I(t_{i-1}) as independent, contradicting the paper's own definition of I(t) as cumulative; this is a correctness issue in the derivation of the t^{-2} scaling, not a circular reduction. Overall, one constructed element in the collapse claim warrants a moderate circularity score, but the central power-law result retains independent fitted content.

Assumptions & free parameters 1 free parameters · 4 assumptions · 0 invented entities

The central claim of a universal collapse depends on one fitted parameter per system (t*) and on the instrument-specific zero-count error floor. Without the t* normalization, the collapse would not occur; without the error floor, the short-time exponent would not be -2.

free parameters (1)
  • characteristic time t* = System-specific; defined by setting the raw mean relative variation to 1 at t = t*
    A per-dataset normalization constant fit to the raw data. It forces each system's curve through the reference point, which is what creates the visual collapse in Fig. 3(a).
assumptions (4)
  • domain assumption Neutron detector counts follow Poisson statistics, with uncertainty sqrt(n) for a pixel count n.
    Used in Eqs. 3-4 to derive the scaling of the metric; standard for neutron detection.
  • ad hoc to paper Detector bins with zero counts are assigned an uncertainty of 1 by the data-reduction software.
    This convention is cited as the origin of the t^{-2} scaling and is specific to the EQSANS reduction pipeline; the claimed exponent range depends on it.
  • domain assumption The scattering intensity I(Q) is a smooth function of Q, justifying the Gaussian Process kernel.
    Inherited from prior work (Ref. 9); not re-validated here, but central to the GPR reconstruction.
  • domain assumption For consecutive time steps, ti is close enough to ti-1 that the normalization factor in Eq. 2 is approximately constant.
    Used in Eqs. 5-8 to obtain the power-law exponents; violated when time bins are widely spaced (e.g., factor-of-3 steps in Fig. 1).

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Cite this review

Pith. "Pith review of Universal Convergence Metric for Time-Resolved Neutron Scattering." pith.science (2026). https://pith.science/paper/7YLYTGOM

@misc{pith2026250513512,
  author       = {Pith},
  title        = {Pith review of: Universal Convergence Metric for Time-Resolved Neutron Scattering},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7YLYTGOM}},
  note         = {Machine review of arXiv:2505.13512}
}
abstract

This work introduces a model-independent, dimensionless metric for predicting optimal measurement duration in time-resolved Small-Angle Neutron Scattering (SANS) using early-time data. Built on a Gaussian Process Regression (GPR) framework, the method reconstructs scattering profiles with quantified uncertainty, even from sparse or noisy measurements. Demonstrated on the EQSANS instrument at the Spallation Neutron Source, the approach generalizes to general SANS instruments with a two-dimensional detector. A key result is the discovery of a dimensionless convergence metric revealing a universal power-law scaling in profile evolution across soft matter systems. When time is normalized by a system-specific characteristic time $t^{\star}$, the variation in inferred profiles collapses onto a single curve with an exponent between $-2$ and $-1$. This trend emerges within the first ten time steps, enabling early prediction of measurement sufficiency. The method supports real-time experimental optimization and is especially valuable for maximizing efficiency in low-flux environments such as compact accelerator-based neutron sources.

Figures

Figures reproduced from arXiv: 2505.13512 by the authors.

Figure 1
Figure 1. FIG. 1. Scattering intensity [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. (a) Dimensionless relative change computed using Eqn. 1 for both raw experimental data (markers) and GPR-inferred scattering [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. (a) Normalized mean relative variation across different soft matter systems, rescaled by system-specific characteristic time [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Confidence intervals derived from linear regression of [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Works this paper leans on

25 extracted references · 19 canonical work pages

  1. [1]

    merlin.mbs aapmrev4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked

    FUNCTION id.bst "merlin.mbs aapmrev4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked" ENTRY address archive archivePrefix author bookaddress booktitle chapter collaboration doi edition editor eid eprint howpublished institution isbn issn journal key language month note number organization pages primaryClass publisher school SLACcitation series title translat...

    2010

  2. [2]

    merlin.mbs aipauth4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked

    FUNCTION id.bst "merlin.mbs aipauth4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked" ENTRY address archive archivePrefix author bookaddress booktitle chapter collaboration doi edition editor eid eprint howpublished institution isbn issn journal key language month note number organization pages primaryClass publisher school SLACcitation series title translat...

    2010

  3. [3]

    merlin.mbs aipnum4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked

    FUNCTION id.bst "merlin.mbs aipnum4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked" ENTRY address archive archivePrefix author bookaddress booktitle chapter collaboration doi edition editor eid eprint howpublished institution isbn issn journal key language month note number organization pages primaryClass publisher school SLACcitation series title translati...

    2010

  4. [4]

    merlin.mbs apsrev4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked

    FUNCTION id.bst "merlin.mbs apsrev4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked" ENTRY address archive archivePrefix author bookaddress booktitle chapter collaboration doi edition editor eid eprint howpublished institution isbn issn journal key language month note number organization pages primaryClass publisher school SLACcitation series title translati...

    2010

  5. [5]

    merlin.mbs apsrmp4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked

    FUNCTION id.bst "merlin.mbs apsrmp4-1.bst 2010-07-25 4.21a (PWD, AO, DPC) hacked" ENTRY address archive archivePrefix author bookaddress booktitle chapter collaboration doi edition editor eid eprint howpublished institution isbn issn journal key language month note number organization pages primaryClass publisher school SLACcitation series title translati...

    2010

  6. [6]

    Lindner \ and\ author T

    author author P. Lindner \ and\ author T. Zemb ,\ @noop title Neutron, X-Ray and Light Scattering: Introduction to an Investigative Tool for Colloidal and Polymetric Systems \ ( publisher North-Holland ,\ address Amsterdam ,\ year 1991 ) NoStop

    work page 1991

  7. [7]

    Lindner \ and\ author T

    author author P. Lindner \ and\ author T. Zemb ,\ @noop title Neutron, X-rays and Light. Scattering Methods Applied to Soft Condensed Matter ,\ edition 1st \ ed.\ ( publisher North-Holland ,\ address Amsterdam ,\ year 2002 ) NoStop

    work page 2002

  8. [8]

    Lindner \ and\ author J

    author author P. Lindner \ and\ author J. Oberdisse ,\ @noop title Neutron, X-rays and Light. Scattering Methods Applied to Soft Condensed Matter ,\ edition 2nd \ ed.\ ( publisher Elsevier ,\ address Amsterdam ,\ year 2025 ) NoStop

    work page 2025

Show all 25 references
  1. [9]

    F \"o rster \ and\ author M

    author author S. F \"o rster \ and\ author M. Antonietti ,\ @noop journal journal Adv. Mater. \ volume 10 ,\ pages 195 ( year 1998 ) NoStop

    1998

  2. [10]

    author author C. K. I. \ Williams \ and\ author C. E. \ Rasmussen ,\ @noop journal journal Adv. Neural Inf. Process. Syst. \ volume 8 ,\ pages 514 ( year 1996 ) NoStop

    1996

  3. [11]

    Seeger ,\ @noop journal journal Int

    author author M. Seeger ,\ @noop journal journal Int. J. Neural Syst. \ volume 14 ,\ pages 69 ( year 2004 ) NoStop

    2004

  4. [12]

    author author C. E. \ Rasmussen \ and\ author C. K. \ Williams ,\ @noop title Gaussian Processes for Machine Learning \ ( publisher MIT Press ,\ address Cambridge ,\ year 2006 ) NoStop

    2006

  5. [13]

    author author V. L. \ Deringer , author A. P. \ Bartók , author N. Bernstein , author D. M. \ Wilkins , author M. Ceriotti , \ and\ author G. Csányi ,\ @noop journal journal Chem. Rev. \ volume 121 ,\ pages 10073 ( year 2021 ) NoStop

    2021

  6. [14]

    \ Tung , author S

    author author C.-H. \ Tung , author S. Yip , author G.-R. \ Huang , author L. Porcar , author Y. Shinohara , author B. G. \ Sumpter , author L. Ding , author C. Do , \ and\ author W.-R. \ Chen ,\ @noop journal journal J. Colloid Interface Sci. \ volume 692 ,\ pages 137554 ( ye...

    2025

  7. [15]

    Jeffreys ,\ @noop title Theory of Probability ,\ edition 3rd \ ed.\ ( publisher Oxford University Press ,\ address Oxford ,\ year 1984 ) NoStop

    author author H. Jeffreys ,\ @noop title Theory of Probability ,\ edition 3rd \ ed.\ ( publisher Oxford University Press ,\ address Oxford ,\ year 1984 ) NoStop

    1984

  8. [16]

    Gelman , author J

    author author A. Gelman , author J. B. \ Carlin , author H. S. \ Stern , author D. B. \ Dunson , author A. Vehtari , \ and\ author D. B. \ Rubin ,\ @noop title Bayesian Data Analysis ,\ edition 3rd \ ed.\ ( publisher CRC Press ,\ address Boca Raton ,\ year 2013 ) NoStop

    2013

  9. [17]

    author author C. N. \ Lam , author C. Do , author Y. Wang , author G.-R. \ Huang , \ and\ author W.-R. \ Chen ,\ @noop journal journal Phys. Chem. Chem. Phys. \ volume 21 ,\ pages 18346 ( year 2019 ) NoStop

    2019

  10. [18]

    \ Huang , author C

    author author G.-R. \ Huang , author C. N. \ Lam , author K. Hong , author Y. Wang , author Y. Shinohara , author C. Do , \ and\ author W.-R. \ Chen ,\ @noop journal journal ACS Macro Lett. \ volume 11 ,\ pages 66 ( year 2022 ) NoStop

    2022

  11. [19]

    James ,\ @noop title Statistical Methods in Experimental Physics ,\ edition 2nd \ ed.\ ( publisher World Scientific ,\ address Singapore ,\ year 2006 ) NoStop

    author author F. James ,\ @noop title Statistical Methods in Experimental Physics ,\ edition 2nd \ ed.\ ( publisher World Scientific ,\ address Singapore ,\ year 2006 ) NoStop

    2006

  12. [20]

    Huang ,\ @noop title Statistical Mechanics ,\ edition 2nd \ ed.\ ( publisher John Wiley & Sons ,\ address New York ,\ year 1991 ) NoStop

    author author K. Huang ,\ @noop title Statistical Mechanics ,\ edition 2nd \ ed.\ ( publisher John Wiley & Sons ,\ address New York ,\ year 1991 ) NoStop

    1991

  13. [21]

    Kardar ,\ @noop title Statistical Physics of Particles \ ( publisher Cambridge University Press ,\ address Cambridge ,\ year 2007 ) NoStop

    author author M. Kardar ,\ @noop title Statistical Physics of Particles \ ( publisher Cambridge University Press ,\ address Cambridge ,\ year 2007 ) NoStop

    2007

  14. [22]

    Anderson , author C

    author author I. Anderson , author C. Andreani , author J. Carpenter , author G. Festa , author G. Gorini , author C.-K. \ Loong , \ and\ author R. Senesi ,\ @noop journal journal Phys. Rep. \ volume 654 ,\ pages 1 ( year 2016 ) NoStop

    2016

  15. [23]

    Brückel , author T

    author author T. Brückel , author T. Gutberlet , author S. Schmidt , author C. Alba-Simionesco , author F. Ott , \ and\ author A. Menelle ,\ @noop journal journal Neutron News \ volume 31 ,\ pages 14 ( year 2020 ) NoStop

    2020

  16. [24]

    @noop title Compact Accelerator Based Neutron Sources ,\ type Tech. Rep. \ number 1981 \ ( institution International Atomic Energy Agency ,\ address Vienna ,\ year 2021 ) NoStop

    1981

  17. [25]

    Ott , author J

    author author F. Ott , author J. Darpentigny , author B. Annighöfer , author M. A. \ Paulin , author J.-L. \ Meuriot , author A. Menelle , author N. Sellami , \ and\ author J. Schwindling ,\ @noop journal journal EPJ Web Conf. \ volume 286 ,\ pages 02001 ( year 2023 ) NoStop

    2023

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