Attribution of gamma-ray background collected by a mobile detector system to its surroundings using panoramic video

Bandstra, Mark S.; Quiter, Brian J.; Curtis, Joseph C.; Bilton, K. J.; Joshi, T. H. Y.; Meyer, Ross; Negut, Victor; Vetter, K.; Archer, D. E.; Hornback, D.; Peplow, Douglas E.; Romano, Catherine; Swinney, Mathew W.; McCullough, Thomas; McLean, Morgan

doi:10.1016/j.nima.2018.08.085

Cited by 14 publications

(23 citation statements)

References 11 publications

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“…As such, NMF provides a natural framework for data fusion, such as incorporating non-radiological contextual information about the environment. Some work on vehicle backgrounds has already shown evidence for the ability to connect NMF components to non-radiological contextual information (i.e., video images) [32]. As a simple example of data fusion for aerial measurements, having knowledge of the distance to a landwater interface would provide useful information about the behavior of the distant and nearby background components.…”

Section: Discussionmentioning

confidence: 99%

Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization

Bandstra

Joshi

Bilton

et al. 2020

IEEE Trans. Nucl. Sci.

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Airborne gamma-ray surveys are useful for many applications, ranging from geology and mining to public health and nuclear security. In all these contexts, the ability to decompose a measured spectrum into a linear combination of background source terms can provide useful insights into the data and lead to improvements over techniques that use spectral energy windows. Multiple methods for the linear decomposition of spectra exist but are subject to various drawbacks, such as allowing negative photon fluxes or requiring detailed Monte Carlo modeling. We propose using Non-negative Matrix Factorization (NMF) as a data-driven approach to spectral decomposition. Using aerial surveys that include flights over water, we demonstrate that the mathematical approach of NMF finds physically relevant structure in aerial gamma-ray background, namely that measured spectra can be expressed as the sum of nearby terrestrial emission, distant terrestrial emission, and radon and cosmic emission. These NMF background components are compared to the background components obtained using Noise-Adjusted Singular Value Decomposition (NASVD), which contain negative photon fluxes and thus do not represent emission spectra in as straightforward a way. Finally, we comment on potential areas of research that are enabled by NMF decompositions, such as new approaches to spectral anomaly detection and data fusion.

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Section: Discussionmentioning

confidence: 99%

Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization

Bandstra

Joshi

Bilton

et al. 2020

IEEE Trans. Nucl. Sci.

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“…This is not fully realistic, as background rates vary both spatially and temporally in urban environments [ 23 , 24 ]. However, a fleet of mobile detectors could map out a spatial and temporal background that could be used for future research [ 25 ].…”

Section: Methodsmentioning

confidence: 99%

Detecting Nuclear Materials in Urban Environments Using Mobile Sensor Networks

Flanagan

Brandt

Osborne

et al. 2021

Sensors

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Radiation detectors installed at major ports of entry are a key component of the overall strategy to protect countries from nuclear terrorism. While the goal of deploying these systems is to intercept special nuclear material as it enters the country, no detector system is foolproof. Mobile, distributed sensors have been proposed to detect nuclear materials in transit should portal monitors fail to prevent their entry in the first place. In large metropolitan areas, a mobile distributed sensor network could be deployed using vehicle platforms such as taxis, Ubers, and Lyfts, which are already connected to communications infrastructure. However, performance and coverage that could be achieved using a network of sensors mounted on commercial passenger vehicles has not been established. Here, we evaluate how a mobile sensor network could perform in New York City using a combination of radiation transport and geographic information systems. The geographic information system is used in conjunction with OpenStreetMap data to isolate roads and construct a grid over the streets. Vehicle paths are built using pickup and drop off data from Uber, and from the New York State Department of Transportation. The results show that the time to first detection increases with source velocity, decreases with the number of mobile detectors, and reaches a plateau that depends on the strength of the source.

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“…Since security scenarios are normally characterized by weak sources or shielded sources, ground-based platforms, such as cars or trucks, are essential to carry large detection systems (∼1 m 2 ) [ 202 ]. However, larger detectors also measure more background events and are more sensitive to background fluctuations [ 202 , 203 ].…”

Section: Mobile Radiation Detection Systemsmentioning

confidence: 99%

“…Bandstra et al [ 202 , 204 ] described the radiological multi-sensor analysis platform (Rad_MAP) system carried by a vehicle ( Figure 14 b) and composed by three types of radiation detectors: A gamma-ray imager with 10 × 10 array of NaI(Tl) detectors with a coded lead mask, an array of 14–24 HPGe detectors (for isotope identification), and an array of 16 liquid scintillators (EJ-209) for fast neutron detection. The Rad_MAP goal is to study variations in the natural background radiation and to serve as a development platform to mobile detection systems and imaging.…”

Section: Mobile Radiation Detection Systemsmentioning

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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Attribution of gamma-ray background collected by a mobile detector system to its surroundings using panoramic video

Cited by 14 publications

References 11 publications

Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization

Modeling Aerial Gamma-Ray Backgrounds Using Non-negative Matrix Factorization

Detecting Nuclear Materials in Urban Environments Using Mobile Sensor Networks

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

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