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REVIEW 3 major objections 6 minor 40 references

Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection

T0 review · 3 major / 6 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read For a fixed sparse set of stationary radiation detectors in a city, how the detector data are combined determines detection performance more than detector count alone, with networked fusion detecting weak cesium sources that an…

desk verdict Credible simulation showing networking helps, but the detection probabilities are not calibrated to the claimed false alarm rate. read the letter →

arxiv 2412.13494 v1 pith:WYBUPVKQ submitted 2024-12-18 physics.ins-det

E. Rofors , N. Abgrall , M.S. Bandstra , R.J. Cooper , D. Hellfeld , T.H.Y. Joshi , V. Negut , B.J. Quiter
show 1 more author
M. Salathe
This is my paper · ORCID
classification physics.ins-det
keywords sparsedetectornetworksurbanradiationdetectiondatafusiongamma-rayspectroscopyvehicleattributefilteringfalsealarmrateplacementoptimizationsimulation
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

Using detailed physics-based simulations of 9,000 vehicles moving through a 6×6 km² area of downtown Chicago, this paper argues that how a sparse set of fixed radiation detectors is operated matters as much as how many detectors are deployed. The central claim: when detectors are fused into a network that correlates a source-carrying vehicle's encounters with several nodes, the network detects weak radioactive sources (5 µCi 137Cs) that an independent detector array fails to detect entirely. For the exposure-optimized layout, the networking methods catch up to 35% of these weak sources while the array catches none. The authors also show that adding visual vehicle attributes from node-mounted cameras, such as color and make, filters uncorrelated background and substantially raises detection probability. If true, this means sparse urban detector networks can approach the performance of denser arrays by combining data and contextual cues rather than by buying more detectors.

What carries the argument

Two mechanisms carry the argument. The first is Algorithm 2, an SNR-optimal fusion test that sorts 1-second detector time-slices by signal-to-noise ratio and adds them, subject to travel feasibility between detector locations (no faster than three times the speed limit), until the combined SNR peaks; if this best-possible spectrum does not alarm, no other combination of measurements could. The second is camera-attribute filtering: when a suspected source pass-by at one detector is linked to a vehicle color and make, neighboring detectors' data are included only for matching vehicles, which strips roughly half the uncorrelated background and raises the fused SNR. These mechanisms are supported by the paper's simulation tool, PyUDM, which combines GEANT4 detector responses, measured urban background spectra, building occlusion from map data, ride-data-derived trip patterns, and city traffic counts to generate realistic detector streams.

What would settle it

Replay weeks of background-only simulated or measured detector streams through Algorithm 2 with the 2.5 SNR threshold and count alarms; if the observed network false alarm rate is materially above one in eight hours, the threshold must be raised and the reported detection probabilities for all three methods will drop, potentially changing which method wins. Alternatively, a controlled field test with a weak 137Cs source driven along known routes through a 200-node deployment would directly measure the detection fractions the paper predicts.

Watch

Extended reading notes

Core claim

The paper's central discovery is that a fixed sparse detector network's detection probability is largely determined by the data-combination strategy, not just by node count or placement. Specifically, in simulations of 1–200 NaI detectors deployed over downtown Chicago, three operating modes are compared: an independent array, a network that uses travel-time windows plus camera-derived vehicle attributes to correlate pass-bys, and a network running an SNR-optimal fusion algorithm (Algorithm 2). The array mode detects none of the 5 µCi 137Cs sources, whereas the networking modes detect up to 35% of them in the exposure-prioritized (σ=0.90) layout. The authors additionally find that detector placement tuned for traffic exposure helps weak sources, while placement tuned for spatial coverage helps strong sources, and that visual attributes such as vehicle color and make cut roughly half the uncorrelated background and markedly increase the fused SNR.

Load-bearing premise

The comparison rests on assuming that applying a single-detector signal-strength threshold of 2.5 to fused multi-detector statistics really yields a whole-network false alarm rate of one alarm per eight hours, even though the paper does not simulate the false-alarm process or account for the search over many detectors and time slices in Algorithm 2.

Editorial extensions

If this is right

  • A sparse network operated with fusion can catch sources that are invisible to the same detectors run as an independent array.
  • Camera-derived vehicle attributes, especially color and make, act as a noise filter that roughly halves uncorrelated background in correlated pass-by searches.
  • For weak sources, placing detectors to maximize traffic exposure (high σ) beats placing them to maximize route coverage; for strong sources the ranking reverses.
  • If the SNR-optimal route does not alarm, no other combination of detector measurements could, so the method bounds the false-negative rate for the modeled encounter.
  • The same number of networked detectors can match or exceed the detection probability of a denser camera-less array, shifting the cost trade-off from hardware to data fusion.

Reading between the lines

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

  • The results suggest that camera metadata should be treated as part of the detection sensor itself; a privacy-conscious deployment could use coarse attributes like color and make to get most of the benefit without license-plate identification.
  • Algorithm 2's focus on maximum fused SNR may systematically overestimate source activity, so any follow-on system that needs source strength estimates would require a separate unbiased estimator.
  • The same fusion logic should transfer to other threat isotopes, shielded sources, or networks with neutron detectors, since the mechanism only requires correlated below-threshold encounters.
  • Because the traffic model uses rideshare trip data, cities without similar data would need alternative route generators, and the quantitative gains may not carry over to cities with very different traffic geometries.
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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 uses the PyUDM simulation tool to compare three operational modes for sparse static radiation detector networks in a 6x6 km area of downtown Chicago: an independent detector array, a network that fuses data based on camera-derived vehicle attributes (color/make), and a network that searches for the SNR-optimal combination of detector time-slices (Algorithm 2). The authors also propose a placement algorithm (Algorithm 1) with a tunable hyperparameter sigma that balances spatial coverage against traffic exposure. The central quantitative claim is that, at a stated whole-network false alarm rate (FAR) of 1 in 8 hours, the network methods detect up to 35% of 5 uCi 137Cs sources in the sigma=0.90 layout while the array detects none. The paper argues that data fusion and contextual information, rather than raw detector count alone, dominate detection performance for weak sources.

Significance. If the results hold at the claimed FAR, the paper would provide a useful engineering guideline for deploying sparse urban detector networks: node placement and data-fusion strategy can matter more than adding detectors. The simulation chain is a strength: it combines GEANT4 detector response, measured NOV Array background spectra, public Chicago traffic/rideshare data, and a documented placement algorithm. PyUDM is stated to be available under an academic license, which supports reproducibility. The distinction between array and network operation is clearly framed. However, the quantitative comparison in Fig. 8 is not yet trustworthy because the false-alarm calibration for the multi-detector statistics is not demonstrated; until that is fixed, the '35% vs 0%' result is an upper-bound estimate with an uncontrolled operating point.

major comments (3)
  1. [Section IV and Section V-D] The whole-network FAR of 1 in 8 hours is asserted, not simulated. The SNR in Eq. (1) is computed from true source counts and true background counts, which are stored separately in the simulation (Section II-B). Under background-only conditions the source count is identically zero, so the SNR statistic is exactly 0 and cannot generate false alarms at any finite threshold. The threshold of 2.5 is taken from a single-detector NMF calibration [38], but none of the three methods in Fig. 8 uses NMF; they all compare the fused SNR to 2.5. The fused statistics involve maxima over detectors (array), over time-slices and travel-time windows (camera method), and over the combinatorial path search (Algorithm 2), so their null distributions are not the single-detector distribution used to establish 2.5. The authors should simulate background-only data for each method and set thresholds that yield a network FAR of 1 in 8 hours, or alternatively present detection probability as a function of threshold/FAR and clearly label the current numbers as nominal rather than calibrated.
  2. [Section V-A and Section V-D] The multiplicity correction for the array is acknowledged but not implemented. The text correctly states in Section V-A that 10 detectors require individual FARs of about 1/80 h to keep the combined FAR at 1/8 h. However, the simulation description in Section V-D gives no indication that per-detector thresholds were raised as a function of N. If the same SNR > 2.5 threshold is applied to every detector, the array's network FAR is approximately N/8 h, which inflates the array's detection probability and biases the comparison against the network methods. The manuscript must state the exact threshold used for the array in the simulations and, if the correction was not applied, rerun the analysis with corrected thresholds.
  3. [Section V-C and Section V-D] The 'optimal path' method is an oracle. As acknowledged in Section V-C, sorting spectra by SNR is possible only because the simulation has access to the true source and background components; a real implementation would have to use an anomaly metric like NMF. The camera-color method similarly appears to use the ground-truth vehicle attributes to select matched time windows (Section V-B), assuming perfect camera identification and association. Therefore the detection probabilities in Fig. 8 for these two methods are upper bounds for idealized algorithms, not achievable performance. The abstract and Section V-D should explicitly state this upper-bound interpretation; the current wording ('the networking methods manage to detect up to 35% of 5 uCi sources') can be read as achievable performance, which is not supported by the simulation design.
minor comments (6)
  1. [Section IV] The mapping from 'SNR threshold of 2.5' to 'FAR of 1 in 8 hours' is quoted from reference [38], but the manuscript does not state the background conditions or detector geometry under which this mapping was established, nor whether it applies to the 2x4x16-inch NaI detectors used here. Please provide more detail or note that the mapping is assumed to carry over.
  2. [Section II-A] Simulating the detector in vacuum (no ground or building scatter) is a simplification; the authors acknowledge it but do not discuss how the missing down-scattered gamma rays might affect the SNR in urban settings. A sentence on the expected impact would help.
  3. [Section V-B] The method description mentions both vehicle color and make, but Fig. 8 labels the method 'Camera color.' Please clarify whether the simulations for Fig. 8 use only color or both color and make, and whether the results are sensitive to this choice.
  4. [Algorithm 2] The pseudocode states 'sorted spectra by high-to-low SNR' without specifying the integration time for each 1-s time slice or how SNR is computed for a single slice. For consistency with Eq. (1), define the counting interval used for each spectrum.
  5. [Section II-B] The sentence 'Gamma-ray background variations from radon progeny rainout and washout [11] are not yet implemented' cites reference [11], which is a general background/anomaly learning paper; it would be clearer to cite the specific source of the rainout/washout phenomenon or leave the citation to the future-work discussion.
  6. [General] The notation '6 × 6 km2' appears inconsistently (also as '6×6 km2' and '6 × 6 km2 area'); please use a consistent format such as '6 km × 6 km' or '36 km^2'.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: central detection results are forward simulations from independent inputs; the oracle nature of Algorithm 2 is explicitly disclosed, and the FAR calibration, while imported rather than simulated, is an external support issue rather than a circular reduction.

full rationale

Walked the derivation chain. The paper's detection probabilities (Sec. V-D, Fig. 8) are produced by simulating independent detector responses (GEANT4), measured background spectra (NOV Array), and real traffic data (OSM/Uber/Google), then applying a fixed SNR threshold. No parameter is fitted to the reported detection probabilities, so the main array-vs-network comparison is not a self-fulfilling fit. Algorithm 2's 'SNR-optimal path' does sort spectra using the true source/background decomposition, but the paper explicitly states this ('we sort the spectra by SNR for computational efficiency, which is possible because we have access to the underlying source and background components of the simulated spectra') and frames the method as an upper bound; this is an acknowledged oracle, not a disguised prediction. The FAR of 1 in 8 hours is taken from a prior measured calibration [38] and not re-simulated for the networked test statistics; that is an unverified assumption or correctness risk, but it is not a circular reduction because the threshold is not derived from the target detection probabilities. Self-citations ([11], [28], [37], [38]) are to methods and measured calibrations, not to uniqueness theorems or forbidden alternatives, so none is load-bearing in the definitional sense. No step reduces by construction to its own inputs.

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

The free parameters and axioms are scenario choices or domain assumptions; none are fitted to force the detection results. The most consequential choices are the 2.5 SNR threshold (imported from single-detector NMF work) and the 100 m pass-by radius, both of which shape the reported detection probabilities.

free parameters (6)
  • Placement priority hyperparameter sigma = 0.01, 0.90 (scanned)
    Balances spatial coverage versus traffic exposure in Algorithm 1; the paper reports results at these two values, and the optimal value depends on the threat scenario, so it is a scenario parameter rather than a fit.
  • Pass-by proximity radius = 100 m
    A vehicle route is counted as passing a detector if it comes within 100 m; chosen based on single-detector simulation results in Section IV and Fig. 4, making it an ad hoc threshold.
  • Time-slice length for SNR-optimal search = 1 s
    Algorithm 2 combines 1 s spectra; the choice is not justified.
  • Travel-speed constraint multiplier = 3x speed limit
    possible_drive allows travel no faster than 3 times the speed limit; this is an ad hoc kinematic prior.
  • False Alarm Rate target = 1 per 8 hours
    Acceptable FAR chosen for the whole network; used to derive the single-detector SNR threshold.
  • NMF detection threshold mapping = SNR = 2.5
    The paper equates the NMF-based anomaly detection threshold at FAR 1/8 h to an SNR threshold of 2.5 via [38], and applies it to networked data without recalibration.
assumptions (6)
  • domain assumption GEANT4 detector response model accurately represents NaI detector response to gamma rays.
    Section II-A: response matrix from GEANT4 simulation is used for all source spectra; no measured-source validation is shown in this paper.
  • domain assumption NOV Array measured background spectra are representative of downtown Chicago backgrounds.
    Section II-B: 1 year of data from Northern Virginia is used to simulate Chicago detector backgrounds; radon washout is not modeled.
  • domain assumption Uber rideshare trip data with Google Maps routes represent the driving patterns of threat vehicles.
    Section II-C: traffic is based on rideshare start/stop and Google Maps recommended routes; the paper notes that this assumes vehicles of interest follow typical traffic patterns.
  • ad hoc to paper Building occlusion is binary; no gamma-ray transmission or scattering through buildings.
    Section II-A states the detector is simulated in a vacuum for simplicity, and Section II-C states that if a building is between source and detector, no gamma rays are registered. This excludes building penetration and down-scatter.
  • ad hoc to paper The greedy SNR-optimal combination reaches the global maximum SNR.
    Section V-C asserts that if the SNR-optimal route does not trigger an alarm, no other combination of spectra could; this is stated without proof and relies on sorting by true SNR.
  • domain assumption Detection via NMF anomaly detection is equivalent to a fixed SNR threshold.
    Section IV maps FAR 1/8 h to SNR threshold 2.5 for NMF via [38]; this mapping is used for all networked methods without recalibrating for search multiplicity.

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Pith. "Pith review of Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection." pith.science (2026). https://pith.science/paper/WYBUPVKQ

@misc{pith2026241213494,
  author       = {Pith},
  title        = {Pith review of: Simulations of Sparse Static Detector Networks for City-Scale Radiological/Nuclear Detection},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WYBUPVKQ}},
  note         = {Machine review of arXiv:2412.13494}
}
abstract

Sparse static detector networks in urban environments can be used in efforts to detect illicit radioactive sources, such as stolen nuclear material or radioactive "dirty bombs". We use detailed simulations to evaluate multiple configurations of detector networks and their ability to detect sources moving through a $6\times6$ km$^2$ area of downtown Chicago. A detector network's probability of detecting a source increases with detector density but can also be increased with strategic node placement. We show that the ability to fuse correlated data from a source-carrying vehicle passing by multiple detectors can significantly contribute to the overall detection probability. In this paper we distinguish static sensor deployments operated as networks able to correlate signals between sensors, from deployments operated as arrays where each sensor is operated individually. In particular, we show that additional visual attributes of source-carrying vehicles, such as vehicle color and make, can greatly improve the ability of a detector network to detect illicit sources.

Figures

Figures reproduced from arXiv: 2412.13494 by the authors.

Figure 1
Figure 1. A sample background spectrum (dotted black), simulated [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Detector encounters for different network size and placement priority [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Two alternate configurations of 200 detectors placed over a [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 4
Figure 4. Figure 4: Simulated SNRs from an unshielded, 1000 µCi [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Bands showing the detection limit for unshielded [PITH_FULL_IMAGE:figures/full_fig_p005_5.png]
Figure 7
Figure 7. Figure 7: Source and background counts and SNR registered by a 200 detector [PITH_FULL_IMAGE:figures/full_fig_p006_7.png]
Figure 6
Figure 6. Figure 6: The timeline of a source-carrying vehicle driving south, passing three [PITH_FULL_IMAGE:figures/full_fig_p006_6.png]
Figure 3
Figure 3. Figure 3: The detection probability is defined as the fraction of the [PITH_FULL_IMAGE:figures/full_fig_p006_3.png]
Figure 8
Figure 8. Figure 8: Probability of detecting a 5 − 100 µCi 137Cs source with detector networks/arrays of increasing size and different layouts. VI. CONCLUSION We have presented an algorithm for optimizing detector placement within sparse static detector networks that can be tuned via a hy…

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Works this paper leans on

40 extracted references · 40 canonical work pages

  1. [38]

    T. J. Aucott, M. S. Bandstra, V . Negut, et al., Effects of background on gamma-ray detection for mobile spectroscopy and imaging systems, IEEE Transactions on Nuclear Science , 61:985–991, 2014

    work page 2014

  2. [1]

    M. J. King, B. Harris, M. Toolin, et al., An urban environment simulation framework for evaluating novel distributed radiation detection architectures, 2010 IEEE International Conference on Technologies for Homeland Security, HST 2010 , pages 446–452, 2010

    work page 2010

  3. [2]

    D. S. Hochbaum and B. Fishbain, Nuclear threat detection with mobile distributed sensor networks, Annals of Operations Research, 187:45–63, 7 2011

    work page 2011

  4. [3]

    Brown, P

    A. Brown, P. Franken, S. Bonner, et al., A highly scalable and autonomous spectroscopic radiation mapping system with resilient iot detector units for dosimetry, safety and security, Journal of Radiological Protection, 43:011503, 1 2023

    work page 2023

  5. [4]

    Schmidt, R

    K. Schmidt, R. C. Smith, J. Hite, et al., Sequential optimal positioning of mobile sensors using mutual information, Statistical Analysis and Data Mining: The ASA Data Science Journal , 12:465–478, 12 2019

    work page 2019

  6. [5]

    R. R. Flanagan, L. J. Brandt, A. G. Osborne, et al., Detecting nuclear materials in urban environments using mobile sensor networks, Sensors 2021, Vol. 21, Page 2196 , 21:2196, 3 2021

    work page 2021

  7. [6]

    R. J. Nemzek, J. S. Dreicer, D. C. Torney, et al., Distributed sensor networks for detection of mobile radioactive sources, IEEE Transactions on Nuclear Science , 51:1693–1700, 8 2004

    work page 2004

  8. [7]

    Chandy, C

    M. Chandy, C. Pilotto, and R. McLean, Networked sensing systems for detecting people carrying radioactive material, Proceedings of INSS 2008 - 5th International Conference on Networked Sensing Systems , pages 148–155, 2008

    work page 2008

Show all 40 references
  1. [8]

    R. R. Flanagan, A. G. Osborne, and M. R. Deinert, Data synthesis improves detection of radiation sources in urban environments, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 1058:168821, 1 2024....

    2024

  2. [9]

    L. M. Wein and M. P. Atkinson, The last line of defense: Designing radiation detection-interdiction systems to protect cities from a nuclear terrorist attack, IEEE Transactions on Nuclear Science , 54:654–669, 6 2007

    2007

  3. [10]

    R. J. Cooper, N. Abgrall, G. Aversano, et al., Networked sensing for radiation detection, localization, and tracking, Journal of Physics: Conference Series, 2586:012125, 9 2023

    2023

  4. [11]

    M. S. Bandstra, N. Abgrall, R. J. Cooper, et al., Background and anomaly learning methods for static gamma-ray detectors, IEEE Transactions on Nuclear Science , 70:2352–2363, 10 2023

    2023

  5. [12]

    Frigo, S

    J. Frigo, S. Brennan, E. Esch, et al., Radiation detection and situa- tion management by distributed sensor networks, Proc. SPIE 7352 , 7352:108–118, 5 2009

    2009

  6. [13]

    Sundaresan, P

    A. Sundaresan, P. K. Varshney, and N. S. Rao, Distributed detection of a nuclear radioactive source using fusion of correlated decisions, 10th International Conference on Information Fusion , 2007

    2007

  7. [14]

    J. C. Chin, N. S. Rao, D. K. Yau, et al., Identification of low-level point radioactive sources using a sensor network, ACM Transactions on Sensor Networks (TOSN) , 7, 10 2010

    2010

  8. [15]

    N. S. V . Rao, C. Y . T. Ma, and D. K. Y . Yau, On performance of individual, collective and network detection of propagative sources, 15th International Conference on Information Fusion , pages 247–254, 2012

    2012

  9. [16]

    C. D. Pahlajani, I. Poulakakis, and H. G. Tanner, Networked decision making for poisson processes with applications to nuclear detection, IEEE Transactions on Automatic Control , 59:193–198, 2014

    2014

  10. [17]

    N. S. Rao, D. Hooper, and J. Ladd-Lively, On feature, classifier and detector fusers for 235u signatures using gamma spectral counts, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 2022

    2022

  11. [18]

    E. W. Bai, A. Heifetz, P. Raptis, et al., Maximum likelihood localization of radioactive sources against a highly fluctuating background, IEEE Transactions on Nuclear Science , 62:3274–3282, 12 2015

    2015

  12. [19]

    Deb, Iterative estimation of location and trajectory of radioactive sources with a networked system of detectors, IEEE Transactions on Nuclear Science, 60:1315–1326, 2013

    B. Deb, Iterative estimation of location and trajectory of radioactive sources with a networked system of detectors, IEEE Transactions on Nuclear Science, 60:1315–1326, 2013

    2013

  13. [20]

    J. M. Hite, J. K. Mattingly, K. L. Schmidt, et al., Bayesian metropolis methods applied to sensor networks for radiation source localization, IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems , 0:389–393, 7 2016

    2016

  14. [21]

    Bukartas, R

    A. Bukartas, R. Finck, J. Wallin, et al., A bayesian method to localize lost gamma sources, Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine, 145:142–147, 3 2019

    2019

  15. [22]

    N. S. Rao, S. Sen, N. J. Prins, et al., Network algorithms for detection of radiation sources, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 784:326–331, 6 2015

    2015

  16. [23]

    Liu and C

    Z. Liu and C. J. Sullivan, Urban source detection with mobile sensor networks enhanced with machine learning algorithms, 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room- Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016 , 2017-January, 10 2017

    2016

  17. [24]

    Abdelhakim, Machine learning for localization of radioactive sources via a distributed sensor network, Soft Computing, pages 1–16, 5 2023

    A. Abdelhakim, Machine learning for localization of radioactive sources via a distributed sensor network, Soft Computing, pages 1–16, 5 2023

    2023

  18. [25]

    M. D. Verlie, D. T. Murnane, A. Butko, et al., Graph neural networks for radiological/nuclear detection with static detector networks, IEEE Nuclear Science Symposium , 12 2023

    2023

  19. [26]

    Agostinelli, J

    S. Agostinelli, J. Allison, K. Amako, et al., Geant4—a simulation toolkit, Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment , 506:250–303, 7 2003

    2003

  20. [27]

    K. M. Gorski, E. Hivon, A. J. Banday, et al., HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere, The Astrophysical Journal , 622:759–771, 4 2005

    2005

  21. [28]

    Abgrall, Y

    N. Abgrall, Y . Ayyad, C. H. Chow, et al., The urban deployment model: A toolset for the simulation and performance characterization of radiation detector deployments in urban environments, Sensors 2024, Vol. 24, Page 4987, 24:4987, 8 2024

    2024

  22. [29]

    20, 2024

    NNDC - National Nuclear Data Center, Available: https://www.nndc.bnl.gov/, Accessed on: Oct. 20, 2024

    2024

  23. [30]

    Hoteling, E

    N. Hoteling, E. T. Moore, W. P. Ford, et al., An analysis of gamma-ray data collected at traffic intersections in Northern Virginia, arXiv:2104.04137 [physics.soc-ph], 3 2021

    2021 arXiv

  24. [31]

    20, 2024., 2024

    OpenStreetMap contributors, OpenStreetMap, Available: https://www.openstreetmap.org, Accessed on: Oct. 20, 2024., 2024

    2024

  25. [32]

    20, 2024

    City of Chicago, Transportation Network Providers - Trips, Chicago Data Portal, Available: https://data.cityofchicago.org/Transportation/Transportation-Network- Providers-Trips/m6dm-c72p, Accessed on: Oct. 20, 2024

    2024

  26. [33]

    20, 2024

    City of Chicago, Average Daily Traffic Counts, Chicago Data Portal, Available: https://data.cityofchicago.org/Transportation/Average- Daily-Traffic-Counts/pfsx-4n4m, Accessed on: Oct. 20, 2024

    2024

  27. [34]

    20, 2024

    TLC Trip Record Data, New York, Available: https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page, Accessed on: Oct. 20, 2024

    2024

  28. [35]

    20, 2024

    City of Chicago, Transportation Network Providers - Vehicles, Chicago Data Portal, Available: https://data.cityofchicago.org/Transportation/Transportation-Network- Providers-Vehicles/bc6b-sq4u, Accessed on: Oct. 20, 2024

    2024

  29. [36]

    A. H. Liu, J. J. Brunn, and K. M. Chandy, Sensor networks for the detection and tracking of radiation and other threats in cities, Proceed- ings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks , 2011

    2011

  30. [37]

    K. J. Bilton, T. H. Joshi, M. S. Bandstra, et al., Non-negative matrix factorization of gamma-ray spectra for background modeling, detection, and source identification, IEEE Transactions on Nuclear Science , 66:827–837, 5 2019

    2019

  31. [39]

    M. R. Marshall, D. Hellfeld, T. H. Joshi, et al., 3-d object tracking in panoramic video and lidar for radiological source-object attribution and improved source detection, IEEE Transactions on Nuclear Science , 68:189–202, 2 2021

    2021

  32. [40]

    M. R. Marshall, R. J. Cooper, J. C. Curtis, et al., Mobile object tracking in panoramic video and lidar for radiological source-object attribution and improved source detection, https://arxiv.org/abs/2309.06592v1, 9 2023

    2023 arXiv

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