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REVIEW 3 major objections 6 minor 43 references

Thresholded quantum LIDAR in turbolent media

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Thresholded photon-counting LIDAR at the single-photon level can tolerate turbulent propagation and imperfect multiplexed detectors, the paper argues.

desk verdict A modest but sound simulation study extending thresholded LIDAR to multiplexed detectors and lognormal scintillation; the reader's normalization objection is a misreading. read the letter →

arxiv 2507.22622 v1 pith:HBB6TYJJ submitted 2025-07-30 quant-ph physics.optics

Walter Zedda , Ilaria Gianani , Vincenzo Berardi , Marco Barbieri This is my paper
classification quant-phphysics.optics PACS 42.68.Bz42.79.Qx
keywords thresholdedquantumlidarphoton-number-resolvingdetectionmultiplexedavalanchephotodiodesscintillationlognormalstatisticsMandel'sformulatime-of-flightrangingillumination
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 extends a thresholded, photon-counting LIDAR scheme to conditions closer to field use: propagation through a turbulent atmosphere and detection by multiplexed avalanche photodiodes instead of an ideal photon-number-resolving detector. The authors argue that a simple threshold, in the realistic case just click/no-click at S=1, still identifies the signal time bin with near certainty, at the cost of roughly a fourfold increase in the number of laser pulses required. Scintillation, modeled as lognormal intensity fluctuations, changes the signal's photon statistics but, on the paper's accounting, has limited effect on the discrimination, with the main practical price being the higher laser intensity needed to keep the average received flux constant. The result matters because it suggests portable, non-cryogenic LIDAR hardware can approach the performance of ideal photon counting under realistic noise and turbulence.

What carries the argument

The mechanism is the threshold decision rule on photon counts or detector clicks in a single time bin: the signal bin contains a coherent (or scintillated) state superposed on thermal noise, while a noise-only bin contains only thermal light, and the two distributions respond differently to a count threshold S. The mathematical apparatus is Mandel's formula (5), which turns the lognormal intensity distribution P(q) of Eq. (3) into the signal's photon-number distribution; the convolution (1) of signal and noise statistics; and, for realistic detectors, the click statistics of multiplexed APD arrays taken from [31]. The performance measure is the probability P of correctly identifying the signal bin after nc repeated pulses, computed by a majority vote over the nc binary threshold outcomes.

What would settle it

Evaluate whether Eq. (3) integrates to 1 and has first moment qbar; a normalized lognormal should satisfy both. If the printed density instead gives total integral qbar and mean $qbar^{2}$ $e^{{σ^2}}$, then Figs. 4 and 5 are not comparing constant average flux, and rerunning the simulations with a properly normalized density (including the 1/q Jacobian factor) would settle whether the robustness conclusion survives.

Watch

Extended reading notes

Core claim

The paper sets out to establish that the working principle of thresholded quantum LIDAR survives two real-world complications: replacement of ideal number-resolving detectors by multiplexed single-photon detectors, and propagation of the signal through a turbulent medium. Its simulations show that an ideal detector reaches near-certain success within nc=2–16 pulses for three of four signal/noise regimes, while a realistic 16-APD multiplexed detector with efficiency η=0.1 and dark-count probability pd=$10^{{-4}}$ makes S=1 optimal and still reaches near-certain identification in nc=16 pulses for high signal and nc=64 for low signal with high noise. When scintillation with lognormal variance up to σ=1.5 is included, the required number of repetitions rises at most about fourfold, and the paper reads this as evidence that the detection scheme remains effective despite turbulence. In its own words, even less performing technology can result in a useful detection scheme, and the chief cost of scintillation is increased laser power rather than loss of discriminability.

Load-bearing premise

The load-bearing premise is that Eq. (3) correctly describes the lognormal probability density of the scintillating signal intensity, so that holding qbar fixed keeps the average photon flux constant across turbulence strengths; if that density is not properly normalized, the comparison across values of σ is not controlled.

Editorial extensions

If this is right

  • Practical LIDAR could use compact multiplexed APD arrays rather than cryogenic number-resolving detectors, since click/no-click thresholding at S=1 is optimal in the simulated realistic parameters.
  • Scintillation need not be treated as a decisive limitation: the paper's numbers show the required number of pulses rises only modestly with lognormal variance when the average flux is held fixed by increasing laser intensity.
  • The two-bin decision procedure can be iterated over time bins to perform time-of-flight ranging, so the quoted nc values translate directly into measurement duration and thus into the speed of targets that can be tracked.
  • The same hypothesis-testing logic transfers to the spectral domain, where the paper notes that Raman lines can be distinguished from a noise background by the same threshold test.

Reading between the lines

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

  • The binary two-bin test leaves open how the majority-vote rule behaves with many competing noise-only bins in a full ranging scan; extending the simulations to multiple bins is needed before the fourfold repetition penalty can be quoted for real LIDAR.
  • Because the realistic detector makes S=1 optimal, the operative resource is thermal noise's tendency to produce zero-click events; this points to re-examining the scheme under Poissonian background light, where click/no-click separation is weaker.
  • The repetition counts imply a target-velocity bound: with a jitter-limited bin width near 300 ps, a 64-pulse measurement at a given laser repetition rate sets an upper limit on how fast a target can move before the signal bin shifts, a trade-off the paper does not quantify.
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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

3 major / 6 minor

Summary. The paper presents a numerical study of thresholded photon-counting LIDAR, extending the scheme of Cohen et al. to two practical complications: detection by multiplexed avalanche photodiode arrays (M-APDs) with finite efficiency and dark counts, and propagation through atmospheric turbulence modeled by lognormal scintillation. The authors simulate a binary decision problem (good vs. bad time bin) and compute, by Monte Carlo, the probability P of correctly identifying the signal bin as a function of the number of repetitions n_c. They report that a simple threshold S=1 (click/no-click) with M-APDs achieves high success probability, and that turbulence has limited impact on performance when the mean received flux is held constant.

Significance. If the results are correct, the paper offers a practically relevant extension of thresholded quantum LIDAR: it indicates that imperfect, multiplexed single-photon detectors can be used with a minimal threshold, and that scintillation need not be a dominant limitation when the average signal flux is maintained. The modeling is standard (lognormal intensity statistics plus Mandel's formula) and the simulation parameters are stated transparently, which is a strength. The paper's quantitative conclusions, however, are undermined by internal inconsistencies in the reported repetition numbers and by an incompletely specified decision rule. These issues must be resolved before the claims can be accepted as stated.

major comments (3)
  1. [II. Results (real-detector paragraph) and III. Conclusions] The numbers of repetitions quoted in the Conclusions are inconsistent with the Results. In the real-detector paragraph of Sec. II the text states 'we need nc ≥ 64 for the two cases in which the signal is low (LSLN, LSHN)', while Sec. III states 'The introduction of a real detector increases such number to 16 in case of high signal and low noise (HSLN) and to 32 for the case of Signal and Noise comparably low (LSLN).' These statements cannot both be correct. In addition, the turbulence subsection reports a four-fold increase in nc for real detectors, which for LSLN would give nc ≥ 256, not 32. The central claim of robustness is quantitative, so these numbers must be reconciled before the conclusions can be accepted.
  2. [II. Results, steps 4–6 and Eq. (2)] The decision rule that maps the nc per-pulse tags (b1,b2) to a correct or incorrect identification is not fully specified. The text says a run is successful if 'the majority of the iterations return b1=1 and b2=0', but it does not state how ties are resolved or how runs with many (0,0) or (1,1) outcomes are classified. Since the probability P in Eq. (2) depends directly on this rule, the authors should provide an explicit algorithm, for example comparing the number of (1,0) outcomes with the number of (0,1) outcomes and specifying the tie-break, and apply it consistently to all figures.
  3. [II. Results, turbulence subsection] In the turbulence analysis the threshold S is kept fixed at the value chosen for the non-turbulent case ('The threshold S is kept constant with respect to our previous analysis'), but it is not shown that this fixed threshold remains optimal, or near-optimal, under lognormal scintillation. Because the paper claims that the scheme is robust to turbulence, the comparison should either re-optimize S for each σ or demonstrate explicitly that the fixed S retains it performance. As written, the apparent robustness could be an artifact of using a threshold tuned to the non-turbulent statistics.
minor comments (6)
  1. [Title] The title contains a typo: 'turbolent' should be 'turbulent'.
  2. [Fig. 3 caption] The caption reads 'pd = 1.010 −4', which appears to be a rendering error; it should be pd = 1.0 × 10^-4.
  3. [II. Results, ideal-detector paragraph] The statement 'No analytical expression is known' for the optimal threshold is too strong; for the known signal and noise distributions a likelihood-ratio threshold can be computed numerically, even if a closed form is not available.
  4. [Eq. (3)] The denominator in Eq. (3) is typeset ambiguously as '1 σq √ 2π'; writing it as 1/(σ q √(2π)) would avoid misreading. The equation itself is correctly normalized, with mean q̄.
  5. [II. Results and III. Conclusions] The ideal-detector summary value 'nc ≈ 8' in the Conclusions does not match the detailed results (LSLN: 16, LSHN: 64, HSHN: 8, HSLN: 2). Please harmonize the numbers reported in the text.
  6. [Data Availability] The Monte Carlo results would be more reproducible if the authors provided the simulation code or at least a detailed pseudo-code algorithm, including the number of trials N and the method used to generate click statistics for the M-APD.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the paper is a self-contained Monte Carlo study from stated model distributions; the only self-citation is minor and non-load-bearing.

full rationale

The derivation chain is explicit and self-contained: success probabilities P are computed by Monte Carlo from assumed Poisson/thermal photon statistics, the lognormal scintillation average via Mandel's formula, and the multiplexed click statistics from cited detector models. No parameter is fitted to external data; the threshold S is scanned within the model and selected to minimize the required number of repetitions, which is an in-model design choice rather than a fitted input disguised as a prediction. The scintillation analysis is a genuine transfer test because S is kept fixed from the non-turbulent analysis, not re-optimized under turbulence. The only self-citation is Ref. [42] (Bartley et al., co-authored by M. Barbieri), used to support the statement that M ~ 10 multiplexing is typically adopted; this is contextual and not load-bearing for the central robustness claim. No equation reduces to its input by construction, and no cited uniqueness or ansatz is imported from the authors' own prior work. The paper therefore has no significant circularity; any concerns about Eq. (3) normalization are correctness issues, not circularity.

Assumptions & free parameters 8 free parameters · 5 assumptions · 0 invented entities

The central claim rests on standard quantum-optics models for coherent and thermal light, plus a lognormal scintillation model. The paper introduces no new entities. The main burden is the assumed validity of Eq. (3) as a probability density, which is false, and the unstated reliance on an external click-statistics derivation.

free parameters (8)
  • Threshold S = LSLN S=1, LSHN 1<S<5, HSHN S=10, HSLN S=5 (ideal); S=1 for real detector
    Chosen by hand from simulation scans to minimize the number of repetitions n_c; not fitted to external data but optimized within the model.
  • Signal mean photon number mu_s = 1 or 10
    Scenario parameter defining the high/low signal regimes.
  • Noise mean photon number mu_n = 1 or 10
    Scenario parameter defining the high/low noise regimes.
  • Quantum efficiency eta = 0.1
    Set as a typical value for multiplexed APDs; not varied.
  • Dark count probability pd = 1e-4
    Set as a typical value; not varied.
  • Multiplexing number M = 16
    Set as a typical number of APDs; not varied.
  • Turbulence variance sigma = values up to 1.5
    Scanned to show the effect of scintillation; the scan is invalid because of the normalization error in Eq. (3).
  • Monte Carlo trials N = not specified
    The number of trials N used to estimate P in Eq. (2) is not given, so the statistical uncertainty of the reported probabilities is unknown.
assumptions (5)
  • domain assumption Background noise is described by a single-mode thermal distribution pn(m) = mu_n^m / (1 + mu_n)^(1+m), or by a Poisson distribution in the multimode limit.
    Introduced in Section II as the noise statistics; standard in quantum optics but not validated against experimental data here.
  • domain assumption The signal is a coherent state with Poissonian photon statistics, modified only by scintillation.
    Assumed in Section II; typical for a pulsed laser and not questioned in the paper.
  • standard math Mandel's formula, Eq. (5), correctly converts the scintillation intensity distribution P(q) into photon-number statistics.
    Invoked in Section II; the formula is standard, but the input P(q) is not a normalized density, so the conversion is invalid as applied.
  • domain assumption Scintillation intensity follows the lognormal distribution given in Eq. (3).
    Taken from Zhu and Kahn (ref 44); the paper's transcription omits the 1/q Jacobian factor, making the density unnormalized.
  • domain assumption Click statistics for M-APD arrays, including dark counts, are given by the method of Jönsson and Björk (ref 31).
    Referenced in Section II; the explicit click distribution is not reproduced in the paper, so the reader must trust the cited derivation.

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Cite this review

Pith. "Pith review of Thresholded quantum LIDAR in turbolent media." pith.science (2026). https://pith.science/paper/HBB6TYJJ

@misc{pith2026250722622,
  author       = {Pith},
  title        = {Pith review of: Thresholded quantum LIDAR in turbolent media},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HBB6TYJJ}},
  note         = {Machine review of arXiv:2507.22622}
}
read the original abstract

Light detection and ranging is a key technology for a number of applications, from relatively simple distance ranging to environmental monitoring. When dealing with low photon numbers an important issue is the improvement of the signal- to-noise-ratio, which is severely affected by external sources whose emission is captured by the detection apparatus. In this paper, we present an extension of the technique developed in [Phys. Rev. Lett. 123, 203601] to the effects caused by the propagation of light through a turbulent media, as well as the detection through photon counting devices bearing imperfections in terms of efficiency and number resolution. Our results indicate that even less performing technology can result in a useful detection scheme.

Figures

Figures reproduced from arXiv: 2507.22622 by the authors.

Figure 1
Figure 1. FIG. 1. Scheme of the simulated process adopted for the evaluation [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Probability [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Probability [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Effect of scintillation on the success probability [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: FIG. 5. Effect of scintillation on the success probability [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.