Simulation and validation of the Mobile Urban Radiation Search (MURS) gamma-ray detector response

Curtis, Joseph C.; Cooper, R. J.; Joshi, T. H. Y.; Cosofret, Bogdan R.; Schmit, Thomas; Wright, John; Rameau, J. D.; Konno, Daisei; Brown, Daniel J.; Otsuka, Forrest; Rappeport, Eric; Marshall, M. R.; Speicher, J.

doi:10.1016/j.nima.2018.08.087

Cited by 15 publications

(12 citation statements)

References 4 publications

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“…This section details experiments exploring how the proposed models predict angle and distance as compared to the reference tables. In the following figures, the accuracy is the number of samples where the exact angle (or distance 5 ) was predicted correctly over the total number of samples. We highlight these results as, in real scenarios, even ±5°angular tolerance may be unacceptable (particularly if the radioactive mental factors.…”

Section: Experiments Overviewmentioning

confidence: 99%

Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

Sheatsley¹,

Durbin²,

Lintereur³

et al. 2022

Preprint

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One of the principal uses of physical-space sensors in public safety applications is the detection of unsafe conditions (e.g., release of poisonous gases, weapons in airports, tainted food). However, current detection methods in these applications are often costly, slow to use, and can be inaccurate in complex, changing, or new environments. In this paper, we explore how machine learning methods used successfully in cyber domains, such as malware detection, can be leveraged to substantially enhance physical space detection. We focus on one important exemplar application-the detection and localization of radioactive materials. We show that the ML-based approaches can significantly exceed traditional table-based approaches in predicting angular direction. Moreover, the developed models can be expanded to include approximations of the distance to radioactive material (a critical dimension that reference tables used in practice do not capture). With four and eight detector arrays, we collect counts of gamma-rays as features for a suite of machine learning models to localize radioactive material. We explore seven unique scenarios via simulation frameworks frequently used for radiation detection and with physical experiments using radioactive material in laboratory environments. We observe that our approach can outperform the standard table-based method, reducing the angular error by 37 % and reliably predicting distance within 2.4 %. In this way, we show that advances in cyber-detection provide substantial opportunities for enhancing detection in public safety applications and beyond.

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Section: Experiments Overviewmentioning

confidence: 99%

Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

Sheatsley¹,

Durbin²,

Lintereur³

et al. 2022

Preprint

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show abstract

“…The mobile urban radiation search (MURS) project [ 208 ] consists of a real-time detection, identification and localization of radiological and nuclear sources in urban environments. The detection system is mounted in a car ( Figure 15 ) and is composed by a six 5.08 × 10.16 × 40.64 cm 3 NaI(Tl) scintillators and a 6 Li neutron sensitivity layer.…”

Section: Mobile Radiation Detection Systemsmentioning

confidence: 99%

“… Vehicle used on the Mobile urban radiation search (MURS) project. Reproduced, with permission, from Reference [ 208 ]; copyright Elsevier (2020). …”

Section: Figurementioning

confidence: 99%

State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios

Marques

Vale

Vaz

2021

Sensors

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In the last decade, the development of more compact and lightweight radiation detection systems led to their application in handheld and small unmanned systems, particularly air-based platforms. Examples of improvements are: the use of silicon photomultiplier-based scintillators, new scintillating crystals, compact dual-mode detectors (gamma/neutron), data fusion, mobile sensor networks, cooperative detection and search. Gamma cameras and dual-particle cameras are increasingly being used for source location. This study reviews and discusses the research advancements in the field of gamma-ray and neutron measurements using mobile radiation detection systems since the Fukushima nuclear accident. Four scenarios are considered: radiological and nuclear accidents and emergencies; illicit traffic of special nuclear materials and radioactive materials; nuclear, accelerator, targets, and irradiation facilities; and naturally occurring radioactive materials monitoring-related activities. The work presented in this paper aims to: compile and review information on the radiation detection systems, contextual sensors and platforms used for each scenario; assess their advantages and limitations, looking prospectively to new research and challenges in the field; and support the decision making of national radioprotection agencies and response teams in respect to adequate detection system for each scenario. For that, an extensive literature review was conducted.

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“…It has been used to acquire extensive background measurements and correlated contextual data that can be used to test algorithms as well as detection and imaging modalities (106). As a result of these developments, more compact systems are now being built for car-borne instruments; these systems consist of similar contextual sensors and several large NaI(Tl) detectors, which are mounted in a vertical barrel-like arrangement to enable enhanced radiation detection and localization up to 20 m (depending on the background, source strength, and speed), reflecting their focus on urban environments (107).…”

Section: Figurementioning

confidence: 99%

The Nuclear Legacy Today of Fukushima

Vetter

2020

Annu. Rev. Nucl. Part. Sci.

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The accident at the Fukushima Daiichi Nuclear Power Station (FDNPS) following the Great East Japan Earthquake and the subsequent tsunami in March 2011 changed people's perceptions regarding nuclear power generation in Japan and worldwide. The failure to prevent the accident and the response to it had an enormous impact specifically on the communities close to the site but also across Japan and globally. In this review, I discuss radiation detection technologies, their use and limits in the immediate assessment and response, and improvements since then. In particular, I examine recent developments in radiation detection and imaging systems that, in combination with the enormous advances in computer vision, provide new means to detect, map, and visualize radiation using manned and unmanned deployment platforms. In addition to smarter and more adaptable technologies to prevent and minimize the impact of such events, an important outcome of this accident is the need for informed and resilient citizens who are empowered by knowledge and technologies to make rational decisions. The accident at FDNPS leaves a legacy concerning the importance of historical information, technologies, and resilience as well as challenges regarding powerful technologies that can provide substantial benefits to human society but that are also associated with risks of which we must be aware. Expected final online publication date for the Annual Review of Nuclear and Particle Science, Volume 70 is October 19, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Simulation and validation of the Mobile Urban Radiation Search (MURS) gamma-ray detector response

Cited by 15 publications

References 4 publications

Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

Improving Radioactive Material Localization by Leveraging Cyber-Security Model Optimizations

State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios

The Nuclear Legacy Today of Fukushima

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