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DOE OSTI2017

The Grism Lens-Amplified Survey from Space (GLASS). XI. Detection of C iv in Multiple Images of the z = 6.11 Lyα Emitter behind RXC J2248.7–4431

The C iii] and C iv rest-frame UV emission lines are powerful probes of the ionization states of galaxies. They have furthermore been suggested as alternatives for spectroscopic redshift confirmation of objects at the epoch of reionization (z>6), where the most frequently used redshift indicator, Lyα, is attenuated by the high fraction of neutral hydrogen in the intergalactic medium. However, currently only very few confirmations of carbon UV lines at these high redshifts exist, making it challenging to quantify these claims. Here, we present the detection of C iv λλ1548, 1551 Å in Hubble Space Telescope slitless grism spectroscopy obtained by GLASS of a Lyα emitter at z = 6.11 multiply imaged by the massive foreground galaxy cluster RXC J2248.7–4431. The C iv emission is detected at the 3σ–5σ level in two images of the source, with marginal detection in two other images. We do not detect significant C iii]λλ1907, 1909 Å emission implying an equivalent width EW{sub CIII]}<20 Å (1σ) and C IV/C III>0.7 (2σ). Combined with limits on the rest-frame UV flux from the He ii λ1640 Å emission line and the O iii]λλ1661, 1666 Å doublet, we put constraints on the metallicity and the ionization state of the galaxy. The estimated line ratios and equivalent widths do not support a scenario where an AGN is responsible for ionizing the carbon atoms. SED fits, including nebular emission lines, imply a source with a mass of log(M/M {sub ⊙}) ∼ 9, SFR of around 10 M {sub ⊙} yr{sup −1}, and a young stellar population <50 Myr old. The source shows a stronger ionizing radiation field than objects with detected C iv emission at z<2 and adds to the growing sample of low-mass (log(M/M {sub ⊙}) ≲ 9) galaxies at the epoch of reionization with strong radiation fields from star formation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

AstroSat /LAXPC Detection of Millisecond Phenomena in 4U 1728-34

The low-mass X-ray binary 4U 1728-24 was observed with AstroSat /LAXPC on 2016 March 8th. Data from a randomly chosen orbit of over 3 ks was analyzed for detection of rapid intensity variations. We found that the source intensity was nearly steady but, toward the end of the observation, a typical Type-1 burst was detected. Dynamical power spectrum of the data in the 3–20 keV band, reveals the presence of a kHz Quasi-Periodic Oscillation (QPO) for which the frequency drifted from ∼815 Hz at the beginning of the observation to about 850 Hz just before the burst. The QPO is also detected in the 10–20 keV band, which was not obtainable by earlier RXTE observations of this source. Even for such a short observation with a drifting QPO frequency, the time lag between the 5–10 and 10–20 keV bands can be constrained to be less than 100 microseconds. The Type-1 burst that lasted for about 20 s had a typical profile. During the first four seconds, dynamic power spectra reveal a burst oscillation for which the frequency increased from ∼361.5 to ∼363.5 Hz. This is consistent with the earlier results obtained with RXTE /PCA, showing the same spin frequency of the neutron star. The present results demonstrate the capability of the LAXPC instrument for detecting millisecond variability even from short observations. After RXTE ceased operation, LAXPC on AstroSat is the only instrument at present with the capability of detecting kHz QPOs and other kinds of rapid variations from 3 keV to 20 keV and possibly at higher energies as well.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

Constraining gamma-ray emission from luminous infrared galaxies with Fermi-LAT; tentative detection of ARP 220

Star-forming galaxies produce gamma-rays primarily via pion production, resulting from inelastic collisions between cosmic-ray protons and the interstellar medium (ISM). The dense ISM and high star formation rates of luminous and ultra-luminous infrared galaxies (LIRGs and ULIRGs) imply that they should be strong gamma-ray emitters, but so far only two LIRGs have been detected. Theoretical models for their emission depend on the unknown fraction of cosmic-ray protons that escape these galaxies before interacting. We analyze Fermi-LAT data for 82 of the brightest Infrared Astronomical Satellite LIRGs and ULIRGs. We examine each system individually and carry out a stacking analysis to constrain their gamma-ray fluxes. We report the detection of the nearest ULIRG Arp 220 (∼4.6σ). We observe a gamma-ray flux (0.8–100 GeV) of 2.4 × 10{sup −10} phot cm{sup −2} s{sup −1} with a photon index of 2.23 (8.2 × 10{sup 41} erg s{sup −1} at 77 Mpc). We also derive upper limits (ULs) for the stacked LIRGs and ULIRGs. The gamma-ray luminosity of Arp 220 and the stacked ULs agree with calorimetric predictions for dense star-forming galaxies. With the detection of Arp 220, we extend the gamma-ray–IR luminosity correlation to the high-luminosity regime with logL{sub 0.1−100GeV}=1.25×logL{sub 8−1000μm}+26.7 as well as the gamma-ray–radio continuum luminosity correlation with logL{sub 0.1−100GeV}=1.22×logL{sub 1.4GHz}+13.3. The current survey of Fermi-LAT is on the verge of detecting more LIRGs/ULIRGs in the local universe, and we expect even more detections with deeper Fermi-LAT observations or the next generation of gamma-ray detectors.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2005

Nighttime Cirrus Detection using Atmospheric Infrared Sounder Window Channels and Total Column Water Vapor

A method of cirrus detection at nighttime is presented that utilizes 3.8 and 10.4 (micro)m infrared (IR) window brightness temperature differences (dBT) and total column precipitable water (PW) measurements. This technique is applied to the Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit A (AMSU-A) instrument suite on board EOS-Aqua, where dBT is determined from sets of carefully selected AIRS window channels, while PW is derived from the synergistic AIRS and AMSU-A water vapor retrievals. Simulated and observed dBT for a particular value of PW are not constant; several physical factors impact dBT, including the variability in temperature and relative humidity profiles, surface emissivity, instrument noise, and skin/ near-surface air temperature differences. We simulate clear-sky dBT over a realistic range of PWs using 8350 radiosondes that have varying temperature and relative humidity profiles. Thresholds between cloudy and uncertain sky conditions are derived once the scatter in the clear-sky dBT is determined. Simulations of optically thin cirrus indicate that this technique is most sensitive to cirrus optical depth in the 10 (micro)m window of 0.1-0.15 or greater over the tropical and subtropical oceans, where surface emissivity and skin/near-surface air temperature impacts on the IR radiances are minimal. The method at present is generally valid over oceanic regions only, specifically, the tropics and subtropics. The detection of thin cirrus, and other cloud types, is validated using observations at the Atmospheric Radiation Measurement (ARM) program site located at Manus Island in the tropical western Pacific for 89 coincident EOS-Aqua overpasses. Even though the emphasis of this work is on the detection of thin cirrus at nighttime, this technique is sensitive to a broad cloud morphology. The cloud detection technique agrees with ARM-detected clouds 82-84% of the time, which include thin cirrus, as well as other cloud types. Most of the disagreements are well explained by AIRS footprint-scale heterogeneity compared to ARM point measurements, cirrus overlying lower-layer water clouds, possible mixed phase microphysics in midlevel clouds, and significant IR channel noise for cold BT scenes over deep convective towers.

Atmospheric Radiation Measurement (RAM)↗

NASA NTRS2008

Automatic Hazard Detection for Landers

Unmanned planetary landers to date have landed 'blind'; that is, without the benefit of onboard landing hazard detection and avoidance systems. This constrains landing site selection to very benign terrain,which in turn constrains the scientific agenda of missions. The state of the art Entry, Descent, and Landing (EDL) technology can land a spacecraft on Mars somewhere within a 20-100km landing ellipse.Landing ellipses are very likely to contain hazards such as craters, discontinuities, steep slopes, and large rocks, than can cause mission-fatal damage. We briefly review sensor options for landing hazard detection and identify a perception approach based on stereo vision and shadow analysis that addresses the broadest set of missions. Our approach fuses stereo vision and monocular shadow-based rock detection to maximize spacecraft safety. We summarize performance models for slope estimation and rock detection within this approach and validate those models experimentally. Instantiating our model of rock detection reliability for Mars predicts that this approach can reduce the probability of failed landing by at least a factor of 4 in any given terrain. We also describe a rock detector/mapper applied to large-high-resolution images from the Mars Reconnaissance Orbiter (MRO) for landing site characterization and selection for Mars missions.

hazard detection↗

NASA NTRS2015

Java Architecture for Detect and Avoid Extensibility and Modeling

Unmanned aircraft will equip with a detect-and-avoid (DAA) system that enables them to comply with the requirement to "see and avoid" other aircraft, an important layer in the overall set of procedural, strategic and tactical separation methods designed to prevent mid-air collisions. This paper describes a capability called Java Architecture for Detect and Avoid Extensibility and Modeling (JADEM), developed to prototype and help evaluate various DAA technological requirements by providing a flexible and extensible software platform that models all major detect-and-avoid functions. Figure 1 illustrates JADEM's architecture. The surveillance module can be actual equipment on the unmanned aircraft or simulators that model the process by which sensors on-board detect other aircraft and provide track data to the traffic display. The track evaluation function evaluates each detected aircraft and decides whether to provide an alert to the pilot and its severity. Guidance is a combination of intruder track information, alerting, and avoidance/advisory algorithms behind the tools shown on the traffic display to aid the pilot in determining a maneuver to avoid a loss of well clear. All these functions are designed with a common interface and configurable implementation, which is critical in exploring DAA requirements. To date, JADEM has been utilized in three computer simulations of the National Airspace System, three pilot-in-the-loop experiments using a total of 37 professional UAS pilots, and two flight tests using NASA's Predator-B unmanned aircraft, named Ikhana. The data collected has directly informed the quantitative separation standard for "well clear", safety case, requirements development, and the operational environment for the DAA minimum operational performance standards. This work was performed by the Separation Assurance/Sense and Avoid Interoperability team under NASA's UAS Integration in the NAS project.

unmanned aircraft systems↗

NASA NTRS2020

Cloud Detection over Snow and Ice with Oxygen A- and B-band Observations from the Earth Polychromatic Imaging Camera (EPIC)

Satellite cloud detection over snow and ice has been difficult for passive remote sensing instruments due to the lack of contrast between clouds and cold/bright surfaces; cloud mask algorithms often heavily rely on shortwave infrared (IR) channels over such surfaces. The Earth Polychromatic Imaging Camera (EPIC) on board the Deep Space Climate Observatory (DSCOVR) does not have infrared channels, which makes cloud detection over snow and ice surfaces even more challenging. This study investigates the methodology of applying EPIC's two oxygen absorption band pair ratios in the A band (764, 780 nm) and B band (688, 680 nm) for cloud detection over the snow and ice surfaces. We develop a novel elevation and zenith-angle-dependent threshold scheme based on radiative transfer model simulations that achieves significant improvements over the existing algorithm. When compared against a composite cloud mask based on geosynchronous Earth orbit (GEO) and low Earth orbit (LEO) sensors, the positive detection rate over snow and ice surfaces increased from around 36 % to 65 % while the false detection rate dropped from 50 % to 10 % for observations of January 2016 and 2017. The improvement in July is less substantial due to relatively better performance in the current algorithm. The new algorithm is applicable for all snow and ice surfaces including Antarctic, sea ice, high-latitude snow, and high-altitude glacier regions. This method is less reliable when clouds are optically thin or below 3 km because the sensitivity is low in oxygen band ratios for these cases.

EPIC↗

NASA NTRS2020

A Machine Learning-Based Cloud Detection and Thermodynamic Phase Classification Algorithm using Passive Spectral Observations

We trained two Random Forest (RF) machine-learning models for cloud mask and cloud thermodynamic phase detection using spectral observations from VIIRS on Suomi NPP (SNPP). Observations from CALIOP were carefully selected to provide reference labels. The two RF models were trained for all-day and daytime-only conditions using a 4-year collocated VIIRS/CALIOP dataset from 2013 to 2016. Due to the orbit difference, the collocated CALIOP and SNPP VIIRS training samples cover a broad viewing zenith angle range, which is a great benefit to overall model performance. The all-day model uses 3 VIIRS infrared (IR) bands (8.6,11, and 12 μm) and the daytime model uses 5 Near-IR (NIR) and Shortwave-IR (SWIR) bands (0.86, 1.24, 1.38, 1.64 and 2.25 μm) together with the 3 IR bands to detect clear, liquid water, and ice cloud pixels. Up to 7 surface types, namely, ocean/water, forest, cropland, grassland, snow/ice, barren/desert, and shrubland, were considered separately to enhance performance for both models. Detection of cloudy pixels and thermodynamic phase with the two RF models were compared against collocated CALIOP products from 2017. It is shown that, with a conservative screening process that excludes the most challenging cloudy pixels for passive remote sensing, the two RF models have high accuracy rates in comparison with the CALIOP reference for both cloud detection and thermodynamic phase. Other existing SNPP VIIRS and Aqua MODIS cloud mask and phase products are also evaluated, with results showing that the two RF models and the MODIS MYD06 optical property phase product are the top 3 algorithms with respect to lidar observations during the daytime. During the nighttime, the RF all-day model works best for both cloud detection and phase, in particular for pixels over snow/ice surfaces. The present RF models can be extended to other similar passive instruments if training samples can be collected from CALIOP or other lidars. However, the quality of reference labels and potential sampling issues that may impact model performance would need further attention.

cloud detection↗

NASA NTRS2021

Porosity Detection and Localization During Composite Cure Inside an Autoclave Using Ultrasonic Inspection

Composite materials offer unique benefits in aerospace applications such as increased strength-to-weight ratio and improved fatigue properties. They are increasingly being used in major commercial aircraft programs. However, current processing methods can lead to defects in composite parts, which must be detected using post-manufacturing inspection methods. Porosity (i.e., pores, voids) is a critical defect from the cure process that is detrimental to performance of the composite part. It is therefore necessary to understand and eliminate the formation of porosity defects. At NASA Langley Research Center, an in-situ cure monitoring system was developed to detect porosity defects as they form in real-time inside an autoclave. A capability to directly detect and localize porosity within a composite during cure did not exist before. This study is focused on an elevated-temperature ultrasonic inspection system to detect porosity defects in composites during autoclave cure. The ultrasonic inspection system operated inside an autoclave within an enclosure cooled by intermittent liquid nitrogen (LN_2) injections. A high-temperature, 2.25 MHz, transducer transmitted ultrasonic waves through the tool plate and into the composite part and measured the amplitude and time of flight of the reflected waves with a 1 mm × 1 mm step size/areal resolution. Porosity was observed via the ultrasonic reflections, which experienced increased attenuation in regions of high porosity. Distinct regions of increased porosity were present due to uneven pressure across the panel, which was driven by an intentional misfit between the flat caul plate and the tapered composite panel with ply drops. The results were validated by post-cure ultrasonic inspection and micrographs. The in-situ inspection system was able to successfully provide porosity detection and localization in the tapered ply-drop panel. The successful results indicate the promise of this system for future implementation in the manufacturing of composite structures for aerospace applications.

Composites↗

NASA NTRS2020

Acoustic space occupancy: Combining ecoacoustics and lidar to model biodiversity variation and detection bias across heterogeneous landscapes

There is global interest in quantifying changing biodiversity in human-modified landscapes. Ecoacoustics may offer a promising pathway for supporting multi-taxa monitoring, but its scalability has been hampered by the sonic complexity of biodiverse ecosystems and the imperfect detectability of animal-generated sounds. The acoustic signature of a habitat, or soundscape, contains information about multiple taxa and may circumvent species identification, but robust statistical technology for characterizing community-level attributes is lacking. Here, we present the Acoustic Space Occupancy Model, a flexible hierarchical framework designed to account for detection artifacts from acoustic surveys in order to model biologically relevant variation in acoustic space use among community assemblages. We illustrate its utility in a biologically and structurally diverse Amazon frontier forest landscape, a valuable test case for modeling biodiversity variation and acoustic attenuation from vegetation density. We use complementary airborne lidar data to capture aspects of 3D forest structure hypothesized to influence community composition and acoustic signal detection. Our novel analytic framework permitted us to model both the assembly and detectability of soundscapes using lidar-derived estimates of forest structure. Our empirical predictions were consistent with physical models of frequency-dependent attenuation, and we estimated that the probability of observing animal activity in the frequency channel most vulnerable to acoustic attenuation varied by over 60%, depending on vegetation density. There were also large differences in the biotic use of acoustic space predicted for intact and degraded forest habitats, with notable differences in the soundscape channels predominantly occupied by insects. This study advances the utility of ecoacoustics by providing a robust modeling framework for addressing detection bias from remote audio surveys while preserving the rich dimensionality of soundscape data, which may be critical for inferring biological patterns pertinent to multiple taxonomic groups in the tropics. Our methodology paves the way for greater integration of remotely sensed observations with high-throughput biodiversity data to help bring routine, multi-taxa monitoring to scale in dynamic and diverse landscapes.

Airborne lidar↗

NASA NTRS2020

Comparison of Cloud Detection Algorithms for Sentinel-2 Imagery

Accurate, automated cloud and cloud shadow detection is a key component of the processing needed to prepare optical satellite imagery for scientific analysis. Many existing cloud detection algorithms rely on temperature information to identify clouds, making detection difficult for imagers that lack a thermal band, like Sentinel-2. To get maximum benefit from Sentinel-2 products it is critical to understand which algorithms best identify clouds and their shadows in images. We examined the relative performance of five different cloud-masking algorithms (Sen2Cor, MAJA, LaSRC, Fmask and Tmask) in 6 Sentinel-2 scenes (28 total images) distributed across the Eastern Hemisphere. Expanding on these comparisons, we tested ensemble approaches to improve results. We tested three ensemble approaches to cloud and shadow classification based on the outputs of the five initial algorithms using the cloud masks in: (1) a majority prediction model; (2) a random forests model; and (3) a conditional logic model. Accuracy assessments show a trade-off between omission and commission errors in cloud detection for individual algorithms across all sites, and some algorithms are better at detecting either clouds or cloud shadows. No single algorithm outperforms the others for both clouds and shadows. Aggregating the results from multiple algorithms produces fewer undetected clouds and higher overall accuracy than any single algorithm, with as high as 2.7% improvement over the top-performing algorithm, suggesting an ensemble approach may be the most useful for processing of Sentinel-2 data.

Sentinel-2↗

NASA NTRSDate not supplied

Assistive Detect and Avoid for Pilots in the Cockpit

Aircraft not receiving radar services rely on see and avoid and radio coordination via Common Traffic Advisory Frequencies to remain well clear of each other and avoid mid-air collisions. Radio coordination is usually performed in the vicinity of non-towered airports whereas non-radar services en-route operations rely solely on see and avoid. This paper presents the results of a simulation study of the effectiveness of assistive detect and avoid technologies when used to enhance pilots’ ability to see and avoid nearby traffic. Three different experimental conditions are modeled, representing “unaided see and avoid”, “see and avoid with traffic advisories”, and “see and avoid with assistive detect and avoid technology”. The effectiveness of see and avoid is evaluated using a set of head-on, crossing, and overtaking encounter scenarios and a model of visual acquisition embedded in a Monte Carlo simulation. The effectiveness of assistive detect and avoid is estimated for the same encounter scenarios. A prototype system for detect and avoid and a summary of results are presented. Preliminary results strongly suggest that assistive detect and avoid could greatly enhance the capabilities of flight crews to avoid traffic and remain well clear.

collision, detect and avoid, resolution, well clea↗

NASA NTRSDate not supplied

Assistive Detect and Avoid for Pilots in the Cockpit

collision↗

NASA NTRSDate not supplied

Machine Learning Application for Improving Cloud Detection and Phase Determination Over Sunglint Regions for Geostationary Satellites

Cloud detection and phase determination over sunglint regions has been a challenge, especially for geostationary (GEO) satellites. Sunglint is observed when the sunlight specular reflection is at the same viewing angle of the satellite sensor. This intense reflection in the visible channels (VIS) is often comparable to that from optically thick clouds. It also contaminates the shortwave infrared channels (SWIR). Consequently, VIS and SWIR channels become less useful - or not useful- when they are saturated, hampering the detection of cloudy and clear-sky pixels. Sunglint contamination happens frequently and exists nearly in every daytime GEO full disk satellite images. However, sunglint intensity and region are difficult to model due to variable viewing geometry and ocean surface conditions. Moreover, existing physical models do not meet the accuracy required for operational GEO satellite cloud detection. We developed a machine learning algorithm to improve cloud detection in sunglint conditions for the NASA Langley’s Satellite ClOud and radiation Property retrieval System (SatCORPS). This poster presents our recent progress in the algorithm development, validation and applications. The algorithm is validated using collocated SatCORPS GOES-East and GOES-West cloud products. We demonstrate that the machine learning cloud detection in sunglint regions is superior to the traditional approach by improving temporal consistency between sunglint and non-sunglint conditions.

Machine Learning, Cloud detection, Sunglint, SatCO↗

DOE OSTI2015

Multi-Level Anomaly Detection on Time-Varying Graph Data

This work presents a novel modeling and analysis framework for graph sequences which addresses the challenge of detecting and contextualizing anomalies in labelled, streaming graph data. We introduce a generalization of the BTER model of Seshadhri et al. by adding flexibility to community structure, and use this model to perform multi-scale graph anomaly detection. Specifically, probability models describing coarse subgraphs are built by aggregating probabilities at finer levels, and these closely related hierarchical models simultaneously detect deviations from expectation. This technique provides insight into a graph's structure and internal context that may shed light on a detected event. Additionally, this multi-scale analysis facilitates intuitive visualizations by allowing users to narrow focus from an anomalous graph to particular subgraphs or nodes causing the anomaly. For evaluation, two hierarchical anomaly detectors are tested against a baseline Gaussian method on a series of sampled graphs. We demonstrate that our graph statistics-based approach outperforms both a distribution-based detector and the baseline in a labeled setting with community structure, and it accurately detects anomalies in synthetic and real-world datasets at the node, subgraph, and graph levels. To illustrate the accessibility of information made possible via this technique, the anomaly detector and an associated interactive visualization tool are tested on NCAA football data, where teams and conferences that moved within the league are identified with perfect recall, and precision greater than 0.786.

anomaly detection↗

DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗

DOE OSTI2013

A Case Study Correlating Innovative Gamma Ray Scanning Detection Systems Data to Surface Soil Gamma Spectrometry Results - 13580

HydroGeoLogic (HGL), Inc. completed a United States Environmental Protection Agency (USEPA) study to characterize radiological contamination at a site near Canoga Park, California. The characterized area contained 470 acres including the site of a prototype commercial nuclear reactor and other nuclear design, testing, and support operations from the 1950's until 1988 [1]. The site history included radiological releases during operation followed by D and D activities. The characterization was conducted under an accelerated schedule and the results will support the project remediation. The project has a rigorous cleanup to background agenda and does not allow for comparison to risk-based guidelines. To target soil sample locations, multiple lines of evidence were evaluated including a gamma radiation survey, geophysical surveys, historical site assessment, aerial photographs, and former worker interviews. Due to the time since production and decay, the primary gamma emitting radionuclide remaining is cesium-137 (Cs-137). The gamma ray survey covered diverse, rugged terrain using custom designed sodium iodide thallium-activated (NaI(Tl)) scintillation detection systems. The survey goals included attaining 100% ground surface coverage and detecting gamma radiation as sensitively as possible. The effectiveness of innovative gamma ray detection systems was tested by correlating field Cs-137 static count ratios to Cs-137 laboratory gamma spectrometry results. As a case study, the area encompassing the former location of the first nuclear power station in the U. S. was scanned, and second by second global positioning system (GPS)-linked gamma spectral data were evaluated by examining total count rate and nuclide-specific regions of interest. To compensate for Compton scattering from higher energy naturally occurring radionuclides (U-238, Th-232 and their progeny, and K-40), count rate ratios of anthropogenic nuclide-specific regions of interest to the total count rate were calculated. From the scanning data, locations with observed Cs-137 ratios exceeding six standard deviations above the mean ratio were mapped in high resolution [2]. Field teams returned to those locations to collect static count measurements using the same detection systems. Soil surface samples were collected at 30 locations and analyzed for Cs-137. An exponential correlation was identified between Cs-137 concentrations in surface soil and field-scanned Cs-137 ratios. The data indicate field minimum detectable concentration (MDC) of Cs-137 at 0.02 Bq/g (0.5 pCi/g) or lower depending on contaminant distribution in soil. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Diamond as a solid state micro-fission chamber for thermal neutron detection at the VR-1 research reactor

Diamond exhibits a combination of properties which makes it attractive for neutron detection in hostile conditions. In the particular case of detection in a nuclear reactor, it is resilient to radiation, exhibits a natural low sensitivity to gamma rays, and its small size (as compared with that of gas ionisation chambers) enables fluency monitoring with a high position resolution. We report here on the use of synthetic CVD diamond as a solid state micro-fission chamber with U-235 converting material for in-core thermal neutron monitoring. Two types of thin diamond detectors were developed for this application. The first type of detector is fabricated using thin diamond membrane obtained by etching low-cost commercially available single crystal CVD intrinsic diamond, so called 'optical grade' material. Starting from a few hundred of micrometre thick samples, the sample is sliced with a laser and then plasma etched down to a few tenths of micrometre. Here we report the result obtained with a 17 μm thick device. The detection surface of this detector is equal to 1 mm{sup 2}. Detectors with surfaces up to 1 cm{sup 2} can be fabricated with this technique. The second type of detector is fabricated by growing successively two thin films of diamond, by the microwave enhanced chemical vapour deposition technique, on HPHT single crystal diamond. A first, a film of boron doped (p+) single crystal diamond, a few microns thick, is deposited. Then a second film of intrinsic diamond with a thickness of a few tens of microns is deposited. This results in a P doped, Intrinsic, Metal structure (PIM) structure in which the intrinsic volume id the active part of the detector. Here we report the results obtained with a 20 μm thick intrinsic whose detection surface is equal to 0.5 mm{sup 2}, with the possibility to enlarge the surface of the detector up to 1 cm{sup 2}. These two types of detector were tested at the VR-1 research reactor at the Czech Technical University in Prague. The Training Reactor VR-1 is a pool type (light water) reactor based on UO{sub 2} low enriched uranium. It has a nominal power of 1 kW, and can be operated for a short period up to 5 kW. The arrangement of the reactor pool reactor facilitates access to the core, setting and removal of various experimental samples and detectors, and safe and easy handling of fuel assemblies. The reactor is equipped with two horizontal channels (radial and tangential) and 10 vertical channels, of varying diameters, which can be loaded into various core positions, and one pneumatic transfer system. It is also equipped with several specifically designed educational instrumentation systems that can be used to supply complementary measurements and characterization around the reactor. The reactor is operated by the Department of Nuclear Reactors of the Faculty of Nuclear Sciences and Physical Engineering of the Czech Technical University in Prague. The two detectors were placed in-core through one of the vertical insertion channel. They were coupled to remote placed (5 m BNC cable) classical nuclear charge sensitive electronics. Detection properties of both sensors, including: pulse height spectra of U-235 fission fragments (response linearity with neutron flux, count rate, gamma background, were evaluated varying the power of the reactor from 0.005 W to 500 W. The evolution of the counting rate of the thinned optical grade detector as a function of counting rate of a gas ionization chamber used currently for reactor monitoring shows the very good linearity of the detector over the 5 decades. Similar results were obtained with the PIM detector. Additionally fast transient current signals of the detectors were recorded on a digital storage oscilloscope (DSO) using broad-band amplifier and with a simple bias-T, showing potential use of such sensors for neutron counting with no need of an amplification stage, since non-amplified signals from fission fragments exceeded 4 mV in amplitude. Therefore, one can think of simple neutron counting system by feeding diamond detectors signals directly to the low threshold discriminators. The results obtained on the VR1 will be described and discussed in detail in the paper and associated presentation. The results demonstrate that diamond micro-fission chambers can be used for in-core neutron monitoring, where robust, simple and compact devices are required.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗