Results for “NATURAL RADIOACTIVITY” — Page 8

DOE OSTI2016

Impact of radon gas concentration in the aerosoles profile

Radon gases relased from building materials and from earth surface are the major responsibility of air ionization. Radon nuclear decay can produce an alpha particle with high energy and Radon progeny. This particle and gamma rays can deliver particles in the air and produce ions with different polarities. This ions, because of induced electric charge, can attach with air aerosols and charge them with their electric charge. The charged aerosols can interact with the other aerosols and ions. Because of this exchange, the air conductivity and the aerosol profiles will change dependently by Radon gas concentration and gamma radiation. Observations show an increase in concentration of Radon during the night, and a decrease during the daylight time. The Radon gas concentration changed hour by hour can induce aerosol profile to change. This dependency between the aerosol profiles and the Radon gas concentrations is discussed.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2015

Nuclear counting filter based on a centered Skellam test and a double exponential smoothing

Online nuclear counting represents a challenge due to the stochastic nature of radioactivity. The count data have to be filtered in order to provide a precise and accurate estimation of the count rate, this with a response time compatible with the application in view. An innovative filter is presented in this paper addressing this issue. It is a nonlinear filter based on a Centered Skellam Test (CST) giving a local maximum likelihood estimation of the signal based on a Poisson distribution assumption. This nonlinear approach allows to smooth the counting signal while maintaining a fast response when brutal change activity occur. The filter has been improved by the implementation of a Brown's double Exponential Smoothing (BES). The filter has been validated and compared to other state of the art smoothing filters. The CST-BES filter shows a significant improvement compared to all tested smoothing filters. (authors)

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Geo-neutrinos and Borexino

Geo-neutrinos, electron anti-neutrinos produced in β-decays of naturally occurring radioactive isotopes in the Earth, are a unique direct probe of our planet’s interior. After a brief introduction about the Earth, the geo-neutrinos’ properties and the main aims of their study are discussed. An overview of the latest experimental results obtained by the Borexino collaboration is provided, followed by a short overview of future perspectives of this new inter-disciplinary field.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Special Nuclear Material Gamma-Ray Signatures for Reachback Analysts

These are slides on special nuclear material gamma-ray signatures for reachback analysts for an LSS Spectroscopy course. The closing thoughts for this presentation are the following: SNM materials have definite spectral signatures that should be readily recognizable to analysts in both bare and shielded configurations. One can estimate burnup of plutonium using certain pairs of peaks that are a few keV apart. In most cases, one cannot reliably estimate uranium enrichment in an analogous way to the estimation of plutonium burnup. The origin of the most intense peaks from some SNM items may be indirect and from ‘associated nuclides.' Indirect SNM signatures sometimes have commonalities with the natural gamma-ray background.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2016

Detection with Enhanced Energy Windowing Phase I Report

This document reviews the progress of Phase I of the Detection with Enhanced Energy Windowing (DEEW) project. The DEEW project is the implementation of software incorporating an algorithm which reviews data generated by radiation portal monitors and utilizes advanced and novel techniques for detecting radiological and fissile material while not alarming on Naturally Occurring Radioactive Material. Independent testing indicated that the Enhanced Energy Windowing algorithm showed promise at reducing the probability of alarm in the stream of commerce compared to existing algorithms and other developmental algorithms, while still maintaining adequate sensitivity to threats. This document contains a brief description of the project, instructions for setting up and running the applications, and guidance to help make reviewing the output files and source code easier.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Process for Conducting a Cost Benefit Analysis for New Radiation Portal Monitors

The Domestic Nuclear Detection Office (DNDO) has deployed over 1,400 radiation portal monitors (RPMs) around the United States to enhance border security. DNDO is now considering deployment of a newer generation of RPMs, intended to have equal or better sensitivity but a considerably lower nuisance alarm rate due to Naturally Occurring Radioactive Material (NORM) that is common in the stream of commerce. If the nuisance alarm rate is sufficiently reduced at a site, US Customs and Border Protection (CBP) may be able to reduce the staffing necessary to respond to nuisance alarms generated by the RPMs. This document provides an overview of some possible processes for conducting a cost benefit analysis to place a reliable dollar amount on the value of these new systems. While there is no hard and fast guide for conducting a cost benefit analysis, an austere method is provided for quick estimating. Two robust methods are also provided, one taking guidance from an Office of Management and Budget circular and the other from the Government Accountability Office.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2015

GADRAS Isotope ID User's Manual for Analysis of Gamma-Ray Measurements and API for Linux and Android

Isotope identification algorithms that are contained in the Gamma Detector Response and Analysis Software (GADRAS) can be used for real-time stationary measurement and search applications on platforms operating under Linux or Android operating systems. Since the background radiation can vary considerably due to variations in naturally-occurring radioactive materials (NORM), spectral algorithms can be substantially more sensitive to threat materials than search algorithms based strictly on count rate. Specific isotopes or interest can be designated for the search algorithm, which permits suppression of alarms for non-threatening sources, such as such as medical radionuclides. The same isotope identification algorithms that are used for search applications can also be used to process static measurements. The isotope identification algorithms follow the same protocols as those used by the Windows version of GADRAS, so files that are created under the Windows interface can be copied directly to processors on fielded sensors. The analysis algorithms contain provisions for gain adjustment and energy linearization, which enables direct processing of spectra as they are recorded by multichannel analyzers. Gain compensation is performed by utilizing photo-peaks in background spectra. Incorporation of this energy calibration tasks into the analysis algorithm also eliminates one of the more difficult challenges associated with development of radiation detection equipment.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2016

An Aerial Survey of the 200 East and 200 West Areas of the Hanford Nuclear Reservation - October 2016

The U.S. Department of Energy (DOE) National Nuclear Security Administration (NNSA) Aerial Measuring System (AMS), based at the Remote Sensing Laboratory (RSL) in Las Vegas, Nevada, performed an aerial survey of the 200 West and 200 East Areas of the DOE Hanford Site during September 2015. The survey was performed using the RSL Bell 412 helicopter equipped with twelve 2"x4"x16" thallium-activated sodium iodide (NaI) logs with their spectra summed to form a single detector and a mechanically-cooled high-purity germanium (HPGe) detector. Both detectors collected gamma ray spectra from naturally-occurring radioactive materials and from anthropogenic radioactive materials present due to activities at the site. Data was collected over the site during the 30 hours of survey flight time, resulting in approximately 100,000 one-second spectra collected by both the NaI array and the HPGe detector. These data have been analyzed to map the distribution of anthropogenic radioactivity and identify the isotopes responsible for that activity.

200 East↗

DOE OSTI2020

Development of Gamma Background Radiation Digital Twin with Machine Learning Algorithms: Application of Unsupervised Machine Learning to Detection of Anomalies and Nuisances in Gamma Background Radiation Environmental Screening Data

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for development of a digital twin of gamma radiation background, and for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. In one segment of work, we developed a gamma background estimation model using a Longshort term memory (LSTM) network for one-step CPS time series prediction. The LSTM model was validated with two data sets of measurements from two independent NaI detectors positioned on a mobile platform. The data sets contained background radiation only and no orphan isotope sources. The LSTM model was constructed and tested using data from one of the detectors. Performance of the LSTM model was validate through one-step prediction of CPS time series of another NaI detector without re-training. This approach allows to create a digital twin for nuclear background estimation. Using LSTM, it could be possible to detect a source through subtraction of the estimated counts from the measured background. In another segment of work, we investigated detection of gamma emitting sources in the presence of complex background using unsupervised machine learning. Spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Detection of Anomalies in Environmental Gamma Radiation Background with Hopfield Artificial Neural Network - Consortium on Nuclear Security Technologies (CONNECT) Q3 Report

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to investigate performance of a Hopfield Neural Network (HNN) in in detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign. One data set contained a 137 Cs source, and another dataset contained a 131 I source.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Development of Hopfield Artificial Neural Network for Anomaly Detection in Environmental Gamma Radiation Background: Consortium on Nuclear Security Technologies (CONNECT) (Q2 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore supervised machine learning (ML) algorithms for development of a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm for detection and identification of weak nuisances and anomalies events in the presence of a highly fluctuating background.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Detection of Anomalies in Gamma Background Radiation Data with K-Means and Self-Organizing Map Clustering Algorithms (Consortium on Nuclear Security Technologies (CONNECT) Q1 Report)

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. The measurement data is a 2D matrix, where one dimension is gamma ray energy, and the other dimension is the number of measurements or total time. In principle, gamma radiation sources can be detected and identified from the measured data by their unique spectral lines. Detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. The objective of this work is to explore unsupervised machine learning (ML) algorithms for detection and identification of weak nuisances and anomalies events in the presence of highly fluctuating background. The challenge is that spectral lines of isotopes are difficult to observe in one-second measurements. Averaging over the entire measurement campaign data set reveals spectral lines of most common background isotopes. Spectral lines of orphan sources, which might appear only in a few measurements during the campaign, will be washed out if averaging is performed over the entire measurement data set. The approach we have explored consists of extracting one-second measurements containing weak spectral features through data clustering. Averaging one-second spectra in a cluster should reveal the presence of anomaly sources. We created two ML models using K-means clustering and Neural Network Self-organizing Map (SOM). Performance of these ML models was benchmarked using search data. One data set contained 137 Cs source, and another dataset contained 131 I source.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2022

Anomaly Detection in Gamma Spectra Using Hopfield Neural Network with B-SAT and Grover’s Algorithm on a Quantum Computing Simulator

Environmental screening of gamma radiation consists of detecting weak nuisance and anomaly signal in the presence of strong and highly varying background. In a typical scenario, a mobile detector-spectrometer continuously measures gamma radiation spectra in short, e.g., one-second, signal acquisition intervals. In principle, gamma radiation sources can be detected and identified by their unique spectral lines. However, detecting sources from data measured in a search scenario is difficult due to the highly varying background because of naturally occurring radioactive material (NORM), and low signal-to-noise ratio (S/N) of spectral signal measured during one-second acquisition intervals. In recent prior work, we have developed a Hopfield Neural Network (HNN) in conjunction with an image processing algorithm to detect a weak signal anomaly hidden among the highly fluctuating background spectra. The objective of this work is to explore quantum computing methods to increase the speed of HNN. The approach is based on the Grover’s search algorithm in conjunction with a 3-SAT problem formalism. The Grover’s algorithm is implemented on a quantum computing simulator using Qiskit software. Performance of HNN algorithm is benchmarked using search data from an environmental screening campaign, where the anomaly is a subset of measurements containing a 137 Cs source. Results indicate that using Grover’s algorithm on a quantum simulator reduces runtime of HNN by two orders of magnitude.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Detection of Isotopes in Urban Source Search Gamma Spectra Using Hopfield Neural Network

Source search campaigns involve the measurement of consecutive background gamma-ray spectra obtained with a mobile detector-spectrometer, such as tellurium-doped sodium iodide NaI(Tl) scintillator detector-spectrometer. Counts are measured with NaI(Tl) detector in short acquisition intervals, such as one second. Detection of isotopes in search data is challenging because of the low signal-to-noise ratio (S/N) of the spectral lines and highly varying background due to naturally occurring radioactive material (NORM). In this work, we investigate the performance of the detection of orphan Cs-137, Am-241, and I-131 isotopes in a gamma ray data set obtained in a search survey. Isotopic detection is performed with a Hopfield Neural Network (HNN), which implements an auto-associative memory by matching incoming measurements to the closest one of its memory-stored signature patterns. HNN is trained on examples of spectra of calibrated isotopic sources with well-resolved spectral lines. During testing, HNN processes spectra with partially complete or distorted isotopic lines. The performance of the HNN detection algorithm is benchmarked with Precision, Recall, and F1 scores. Test results demonstrate the potential of HNN to detect the isotope of interest with high accuracy (F1 > 90%) for all isotopes in this study.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2021

Simulation of radon-222 with the GEOS-Chem global model: Emissions, seasonality, and convective transport

Radon-222 (222Rn) is a short-lived radioactive gas naturally emitted from land surface, and has long been used to assess convective transport in atmospheric models. In this study, we simulate 222Rn using the GEOS-Chem chemical transport model with aims to improve our understanding of 222Rn emissions and surface concentration seasonality, and to characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, MERRA and GEOS-FP. We evaluate four available 222Rn emission scenarios by comparing model results with surface observations at 51 global surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with global surface observations (< 70% data within a factor of 2) and a large underestimate of wintertime surface 222Rn concentrations at Northern Hemisphere mid- and high-latitudes due to an oversimplified formulation of 222Rn emission fluxes (1 atom cm-2 s-1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global 222Rn emission scenario based on Zhang et al. (2011) and show its potential to improve simulated surface 222Rn concentrations and seasonality. The regional components of this emission scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors to 222Rn emission flux rates. However, large model underestimates of surface 222Rn concentrations still exist in Asia, suggesting unusually high regional 222Rn emissions. We propose a conservative up-scaling factor of 1.2 for 222Rn emission fluxes in China, as also constrained by the observed deposition fluxes of 210Pb (decay daughter of 222Rn). With this modification, the model shows better agreement with the observations in Europe and North America (>80% data within a factor of 2), and reasonable agreement in Asia (close to 70%). Further constraints on 222Rn emissions would require additional observations of surface 222Rn concentrations and emission fluxes in central U.S., Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed 222Rn vertical profiles during northern mid-latitude summertime and from two short-term airborne campaigns. While the simulations with both GEOS products are able to capture the observed vertical gradient of 222Rn concentrations in the lower troposphere (0-4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared to GEOS-FP, MERRA leads to stronger convective transport of 222Rn, which is partially compensated by its weaker large-scale vertical advection, resulting in similar global vertical distributions of 222Rn concentrations between the two simulations.

Zhang, Bo↗

NASA NTRS1973

Production of carbon monoxide by charged particle deposition.

Recent studies of electron energy deposition in CO2 and CO based upon a large set of electron impact cross sections are utilized to estimate the telluric CO directly produced by various charged-particle deposition mechanisms. The mechanisms considered are (1) lightning, (2) cloud coronal discharges, (3) background radioactivity, (4) natural electrostatic discharges, (5) photoelectrons in the ionosphere, (6) auroral electrons, (7) auroral protons, (8) cosmic rays, and (9) solar wind. 'Ball park' estimates of the global CO production by each of these mechanisms are given. Apart from mechanisms 1, 2, and 5, all CO production mechanisms are estimated to be small compared to artificial sources. If, as appears to be the case, the hot oxygen atoms and ions and other atomic species immediately produced by these three charged-particle deposition mechanisms react rapidly with CO2 to produce CO, these mechanisms can readily lead to CO production levels in the multimegaton-per-year range.

Green, A. E. S.↗

NASA NTRS2021

Simulation of Radon-222 with the GEOS-Chem Global Model: Emissions, Seasonality, and Convective Transport

Radon-222 (Rn-222) is a short-lived radioactive gas naturally emitted from land surfaces and has long been used to assess convective transport in atmospheric models. In this study, we simulate Rn-222 using the GEOS-Chem chemical transport model to improve our understanding of Rn-222 emissions and surface concentration seasonality and characterize convective transport associated with two Goddard Earth Observing System (GEOS) meteorological products, the Modern-Era Retrospective analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We evaluate four global Rn-222 emission scenarios by comparing model results with observations at 51 surface sites. The default emission scenario in GEOS-Chem yields a moderate agreement with surface observations globally (68.9 % of data within a factor of 2) and a large underestimate of winter surface Rn-222 concentrations at Northern Hemisphere midlatitudes and high latitudes due to an oversimplified formulation of Rn-222 emission fluxes (1 atom cm−2 s−1 over land with a reduction by a factor of 3 under freezing conditions). We compose a new global Rn-222 emission scenario based on Zhang et al. (2011) and demonstrate its potential to improve simulated surface Rn-222 concentrations and seasonality. The regional components of this scenario include spatially and temporally varying emission fluxes derived from previous measurements of soil radium content and soil exhalation models, which are key factors in determining Rn-222 emission flux rates. However, large model underestimates of surface Rn-222 concentrations still exist in Asia, suggesting unusually high regional Rn-222 emissions. We therefore propose a conservative upscaling factor of 1.2 for Rn-222 emission fluxes in China, which was also constrained by observed deposition fluxes of 210Pb (a progeny of Rn-222). With this modification, the model shows better agreement with observations in Europe and North America (> 80 % of data within a factor of 2) and reasonable agreement in Asia (close to 70 %). Further constraints on Rn-222 emissions would require additional concentration and emission flux observations in the central United States, Canada, Africa, and Asia. We also compare and assess convective transport in model simulations driven by MERRA and GEOS-FP using observed Rn-222 vertical profiles in northern midlatitude summer and from three short-term airborne campaigns. While simulations with both GEOS products are able to capture the observed vertical gradient of Rn-222 concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment, resulting in biases in the middle and upper troposphere. Compared with GEOS-FP, MERRA leads to stronger convective transport of Rn-222, which is partially compensated for by its weaker large-scale vertical advection, resulting in similar global vertical distributions of Rn-222 concentrations between the two simulations. This has important implications for using chemical transport models to interpret the transport of other trace species when these GEOS products are used as driving meteorology.

Bo Zhang↗

DOE OSTI2017

Quantitative Digital Autoradiography for Environmental Swipe Sample Prioritization: System design, Characterization, and Initial Measurements

Environmental sampling and sample analyses by the International Atomic Energy Agency’s (IAEA) Network of Analytical Laboratories (NWAL) is a critical technical tool used to detect facility misuse under a Comprehensive Safeguards Agreement and to verify the absence of undeclared nuclear material activities under an Additional Protocol. Currently all environmental swipe samples (ESS) are screened using gamma spectrometry and x-ray fluorescence to estimate the amount of U and/or Pu in the ESS, to guide further analysis, and to assist in the shipment of ESS to the NWAL. Quantitative Digital Autoradiography for Environmental Samples (QDARES) is being developed to complement existing techniques through the use of a portable, real-time, high-spatial-resolution camera called the Ionizing-radiation Quantum Imaging Detector (iQID). The iQID constructs a spatial map of radionuclides within a sample or surface in real-time as charged particles (betas) and photons (gamma/x-rays) are detected and localized on an event-by-event basis. Knowledge of the location and nature of radioactive hot spots on the ESS could provide information for subsequent laboratory analysis. As a nondestructive technique, QDARES does not compromise the ESS chain of custody or subsequent laboratory analysis. In this paper we will present the system design and construction, characterization measurements with calibration sources, and initial measurements of ESS.

McDonald, Benjamin S.↗