Efficient calculation of detection probabilities

Thoreson, Gregory G.; Schneider, Erich

doi:10.1016/j.nima.2010.02.003

Cited by 6 publications

(2 citation statements)

References 12 publications

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“…The detection probability for a mass reading is found by integrating the cumulative distribution function for the Gaussian distribution from the threshold to positive infinity, resulting in Equation 4.2. A thorough derivation of Equations 4.1 and 4.2 is given in [38].…”

Section: Detector-type Safeguardsmentioning

confidence: 99%

A Game Theoretic Approach to Nuclear Safeguards Selection and Optimization

Ward

Schneider

2016

Science & Global Security

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Section: Detector-type Safeguardsmentioning

confidence: 99%

A Game Theoretic Approach to Nuclear Safeguards Selection and Optimization

Ward

Schneider

2016

Science & Global Security

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“…We briefly review how we model threat scenarios and compute associated detection probabilities. For further details see the discussion in Dimitrov et al [14,15] and Thoreson and Schneider [42]. We specify a threat scenario via the smuggler's origindestination (O-D) pair as well as: (i) the type of nuclear material being smuggled, e.g., weapons-grade plutonium, highly-enriched uranium, and spent nuclear fuel; (ii) the material's mass of the material; (iii) shielding, e.g., lead and borated-polyethylene; and, (iv) the transporting vehicle.…”

Section: Computing Detection Probabilitiesmentioning

confidence: 99%

Prioritizing Network Interdiction of Nuclear Smuggling

Michalopoulos¹,

Morton

Barnes³

2012

Stochastic Programming

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We develop a stochastic network interdiction model for prioritizing locations for installing radiation detectors along a nation's border. In this one-country model, we characterize the smuggler population by a set of possible threat scenarios, where the identity of the smuggler is unknown at the time we install detectors. Detector performance depends on the threat scenario, as well as a number of additional factors such as terrestrial background radiation, geometric attenuation, and exposure time. Furthermore, the budget for installing detectors is unknown at the time the installation plan must be proposed. We model the budget as having a known probability distribution, and consequently, the solution to the problem is a rank-ordered priority list of installation locations, where one or more locations are assigned to each priority level. Upon its realization, we exhaust the budget by installing detectors at locations ranked from highest to lowest priority. The identity of the smuggler is subsequently revealed. Having full knowledge of the interdictor's actions, the smuggler then selects an origin-destination path, which maximizes his evasion probability. Modeling the problem as a bilevel stochastic mixed-integer program, we present methods for strengthening the resulting formulation, exact and heuristic solution algorithms, and computational results. We also introduce a performance measure that quantifies the value of our prioritization model.

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