Results for “gamma detectors” — Page 28

DOE OSTI2023

A portable and monoenergetic 24 keV neutron source based on 124Sb-9Be photoneutrons and an iron filter

A portable monoenergetic 24 keV neutron source based on the 124Sb-9Be photoneutron reaction and an iron filter has been constructed and characterized. The coincidence of the neutron energy from SbBe and the low interaction cross-section with iron (mean free path up to 29 cm) makes pure iron specially suited to shield against gamma rays from 124Sb decays while letting through the neutrons. To increase the 124Sb activity and thus the neutron flux, a >1 GBq 124Sb source was produced by irradiating a natural Sb metal pellet with a high flux of thermal neutrons in a nuclear reactor. The design of the source shielding structure makes for easy transportation and deployment. A hydrogen gas proportional counter is used to characterize the neutrons emitted by the source and a NaI detector is used for gamma background characterization. At the exit opening of the neutron beam, the characterization determined the neutron flux in the energy range 20–25 keV to be 6.00±0.30 neutrons per cm2 per second and the total gamma flux to be 245±8 gammas per cm2 per second (numbers scaled to 1 GBq activity of the 124Sb source). A liquid scintillator detector is demonstrated to be sensitive to neutrons with incident kinetic energies from 8 to 17 keV, so it can be paired with the source as a backing detector for neutron scattering calibration experiments. This photoneutron source provides a good tool for in-situ low energy nuclear recoil calibration for dark matter experiments and coherent elastic neutrino-nucleus scattering experiments.

Dark Matter detectors (WIMPs, axions, etc.)↗

DOE OSTI2022

Silicon Photomultipliers Coupled to Scintillators With the Emission Maximum at 550 nm

A majority of the silicon photomultipliers (SiPMs) are sensitive to blue and near-ultraviolet (NUV) photons that are not optimized for scintillators with the emission maximum at wavelengths longer than 500 nm. The red–green–blue (RGB) SiPM is developed for the maximum photon detection efficiency (PDE) at 550 nm, which is a good match for some high-light-yield scintillators, such as CsI:Tl (CsI) and Gd 1.5 Y 1.5 Ga 2 Al 3 O 12 :Ce (GYGAG). Comparisons are made for the performance of these scintillators coupled to the RGB SiPM and a blue-sensitive SiPM. Because it takes tens of nanoseconds for the microcells to recharge after registering a photon hit, the linearity of these scintillation detectors was studied for high-energy gammas where numerous scintillation photons are generated. In addition, the energy resolution of the 662-keV gamma emitted by 137 Cs was measured for temperatures between –20 °C and 50 °C. The nonlinearity was observed above 1 MeV for all measurements, and however, it can be corrected by energy calibration using a third-degree polynomial. For CsI, the energy resolution is better with the blue-sensitive SiPM because of the lower dark count rate (DCR). In contrast, GYGAG coupled to the RGB SiPM has a better energy resolution for temperatures below 30 °C because of the well-matched emission spectrum and PDE distribution. Nevertheless, the advantage disappears for temperatures above 30 °C due to the higher DCR. It would be useful to further develop the RGB SiPM with a lower DCR and higher operating temperatures.

42 ENGINEERING↗

NASA NTRS1988

Hard X-ray and low-energy gamma-ray spectrometers

Basic principles of operation and characteristics of scintillation and semi-conductor detectors used for solar hard X-ray and gamma-ray spectrometers are presented. Scintillation materials such as NaI offer high stopping power for incident gamma rays, modest energy resolution, and relatively simple operation. They are, to date, the most often used detector in solar gamma-ray spectroscopy. The scintillator BGO has higher stopping power than NaI, but poorer energy resolution. The primary advantage of semi-conductor materials such as Ge is their high-energy resolution. Monte-Carlo simulations of the response of NaI and Ge detectors to model solar flare inputs show the benefit of high resoluton for studying spectral lines. No semi-conductor material besides Ge is currently available with adequate combined size and purity to make general-use hard X-ray and gamma-ray detectors for solar studies.

Gehrels, N.↗

NASA NTRS2012

Chemical Mapping of Vesta and Ceres

Following successful science operations at Vesta, the Dawn spacecraft is headed for an encounter with Ceres in 2015. What have we learned at Vesta? And, what do we expect to learn by comparing Vesta and Ceres? We will address these questions from the standpoint of geochemistry. Dawn's Gamma Ray and Neutron Detector (GRaND) is sensitive to the elemental composition of surface materials to depths of a few decimeters [1]. Gamma rays and neutrons, produced by the steady bombardment of galactic cosmic rays and by the decay of naturally ]occurring radioisotopes (K, Th, U), provide a chemical fingerprint of the regolith. Analysis of planetary radiation emissions enables mapping of specific elements (such as Fe, Mg, Si, Cl, and H) and compositional parameters (such as average atomic mass), which provide information about processes that shaped the planet1s surface and interior. Dawn has exceeded operational goals for GRaND at Vesta, accumulating an abundance of nadir-pointed data during five months in a 210 km, low altitude mapping orbit around Vesta (265-km mean radius). Chemical information from gamma ray and neutron measurements was used to test the connection between Vesta and the howardite, eucrite, and diogenite (HED) meteorites [2]. Additionally, GRaND searched for evolved, igneous lithologies [3], mantle and upper crustal materials exposed in large impact basins, mesosiderite compositions, and hydrogen in Vesta1s bulk regolith. Results of our analyses and their implications for thermal evolution and regolith-processes will be presented. The possibility of a subcrustal ocean [4, 5] and lack of cerean meteorites makes water-rich Ceres a compelling target of exploration [6]. If Ceres underwent aqueous differentiation, then crustal overturn or gas driven volcanism may have significantly modified its primitive surface; and products of aqueous alteration (e.g. [7]) would detectable by GRaND [1]. For example, the presence of Cl in salts, associated with liquid-water-processes, would have a profound effect on the thermal neutron leakage flux. GRaND is sensitive to H and H-layering, which may be in the form of endogenic water ice or hydrous minerals on Ceres. Ammonia ice (e.g., from recent cryovolcanism) would produce a distinctly different neutron signature than water ice [1]. Prospective results for GRaND at Ceres will be presented in the context of what we have learned about Vesta.

Prettyman, Thomas H.↗

NASA NTRS1977

Inclusive gamma-ray spectra from psi/3095/ and psi-prime/3684/ decays

Inclusive gamma-ray experiments were carried out in a e(+)e(-) colliding-beam apparatus with NaI(Tl) arrays as detectors. The inclusive gamma-ray spectra, after cosmic-ray background subtraction, are shown as histograms for the decays of the psi(3095) and psi-prime(3684). The psi spectrum has no significant narrow structure, while the psi-prime spectrum shows at least four peaks. Three major radiative decays of the psi-prime(3684) are found, and their respective branching fractions are computed.

Biddick, C. J.↗

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 OSTI2024

Radiological Mapping of Nuclear Facilities

Identifying radiological abnormalities is critical during emergencies at nuclear facilities. This facilitates a necessity for the Emergency Management (EM) to map baseline radiation levels at the Idaho National Laboratory (INL). The INL uses scintillation detectors to measure radioactivity and gamma spectrums; but, because they are heavy and costly, these detectors can be impractical for mapping radiological areas. This presentation will establish initial radiological heat maps for emergencies and test the Radiacode-103G — a handheld scintillator with GPS and Bluetooth capability with a Gadolinium Aluminum Gallium Garnet (GAGG) crystal.

61 RADIATION PROTECTION AND DOSIMETRY↗

NASA NTRS1991

Segmented Ge detector rejection of internal beta activity produced by neutron irradiation

Future Ge spectrometers flown in space to observe cosmic gamma-ray sources will incorporate segmented detectors to reduce the background from radioactivity produced by energetic particle reactions. To demonstrate the effectiveness of a segmented Ge detector in rejecting background events due to the beta decay of internal radioactivity, a laboratory experiment has been carried out in which radioactivity was produced in the detector by neutron irradiation. A Cf-252 source of neutrons was used to produce, by neutron capture on Ge-74 (36.5 percent of natural Ge) in the detector itself, Ge-75 (t sub 1/2 = 82.78 min), which decays by beta emission with a maximum electron kinetic energy of 1188 keV. By requiring that an ionizing event deposit energy in two or more of the five segments of the detector, each about 1-cm thick, the beta particles, which have a range of about 1-mm, are rejected, while most external gamma rays incident on the detector are counted. Analysis of this experiment indicates that over 85 percent of the beta events from the decay of Ge-75 are rejected, which is in good agreement with Monte Carlo calculations.

Varnell, L. S.↗

DOE OSTI2020

Solid Cherenkov detector for studying nucleosynthesis in inertial confinement fusion

Measuring gamma rays emitted from nuclear reactions gives insight into their nuclear structure. Notably, there are several nuclear reactions that produce gamma rays at ~1 MeV–3 MeV energies such as T( 4 He, γ) 7 Li, 4 He( 3 He, γ) 7 Be, and 12 C(p, γ) 13 N, which may solve questions lingering about big-bang nucleosynthesis and stellar nucleosynthesis. To observe 1 MeV–3 MeV gamma rays in an inertial confinement fusion system, a new style of the Cherenkov detector was developed using aerogel and fused silica as a Cherenkov medium. Utilizing the OMEGA laser facility, both aerogel and fused silica media were compared with the existing gas-medium Cherenkov detector to validate the concept. Furthermore, gamma ray measurements from high yield inertial confinement fusion implosions (deuterium–tritium and deuterium– 3 He) demonstrated that aerogel and fused silica were viable Cherenkov media, paving the way for a potential optimized detector to make these cross section measurements on OMEGA or the National Ignition Facility.

47 OTHER INSTRUMENTATION↗

NASA NTRS1988

Gamma ray astrophysics to the year 2000. Report of the NASA Gamma Ray Program Working Group

Important developments in gamma-ray astrophysics up to energies of 100 GeV during the last decade are reviewed. Also, the report seeks to define the major current scientific goals of the field and proposes a vigorous program to pursue them, extending to the year 2000. The goals of gamma-ray astronomy include the study of gamma rays which provide the most direct means of studying many important problems in high energy astrophysics including explosive nucleosynthesis, accelerated particle interactions and sources, and high-energy processes around compact objects. The current research program in gamma-ray astronomy in the U.S. including the space program, balloon program and foreign programs in gamma-ray astronomy is described. The high priority recommendations for future study include an Explorer-class high resolution gamma-ray spectroscopy mission and a Get Away Special cannister (GAS-can) or Scout class multiwavelength experiment for the study of gamma-ray bursts. Continuing programs include an extended Gamma Ray Observatory mission, continuation of the vigorous program of balloon observations of the nearby Supernova 1987A, augmentation of the balloon program to provide for new instruments and rapid scientific results, and continuation of support for theoretical research. Long term recommendations include new space missions using advanced detectors to better study gamma-ray sources, the development of these detectors, continued study for the assembly of large detectors in space, collaboration with the gamma-ray astronomy missions initiated by other countries, and consideration of the Space Station attached payloads for gamma-ray experiments.

Source record↗

DOE OSTI2023

$\gamma$-to-neutron branching ratio for deuterium-tritium fusion determined using high-energy-density plasmas and a fused silica Cherenkov detector

A fused silica Cherenkov detector was used to measure deuterium-tritium (DT) gammas during a set of 52 direct-drive cryogenic experiments performed at OMEGA. The detector was calibrated using the 4.4 MeV$\gamma$ from the first-excited state of carbon, which is produced when 14-MeV DT neutrons impinge upon a carbon puck. An approximate DT $\gamma$ spectrum as well as neutron yields from a standard neutron time-of-flight detector at OMEGA were used to calculate a DT $\gamma$-to-neutron branching ratio of (8.42 ± 2.84 ) x 10 -5 . Assuming an excited-state to ground-state ratio of 2.1:1, the measurement detailed in this work results in an approximate ground state only$\gamma$-to-neutron branching ratio of 2.72 x 10 -5 . So, this value is somewhat lower than accelerator-based measurements of the ground-state DT $\gamma$ only.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1985

A thick Anger camera for gamma-ray astronomy

The NaI(Tl) Anger camera is a natural candidate for a position sensitive detector in imaging of astrophysical gamma-ray sources. Here laboratory measurements are presented of the response of a relatively thick (5.1 cm) NaI(Tl) Anger camera designed for coded aperture imaging in the 50 keV to 2 MeV energy range. A position resolution of 10.5 mm FWHM at 122 keV and 6.3 mm FWHM at 662 keV. The energy resolution was 7 percent FWHM at 662 keV. The ability of the detector to resolve the depth of the gamma-ray interaction and the use of this depth resolution to reduce back-incident and internal background is discussed.

Cook, W. R.↗

DOE OSTI2024

Studies of new Higgs boson interactions through nonresonant $HH$ production in the $ b\overline{b}\gamma \gamma $ final state in $pp$ collisions at $ \sqrt{s} $ = 13 TeV with the ATLAS detector

A search for nonresonant Higgs boson pair production in the $ b\overline{b}\gamma \gamma $ final state is performed using 140 fb –1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. This analysis supersedes and expands upon the previous nonresonant ATLAS results in this final state based on the same data sample. The analysis strategy is optimised to probe anomalous values not only of the Higgs (H) boson self-coupling modifier κλ but also of the quartic HHVV (V = W, Z) coupling modifier κ 2V . No significant excess above the expected background from Standard Model processes is observed. An observed upper limit μHH < 4.0 is set at 95% confidence level on the Higgs boson pair production cross-section normalised to its Standard Model prediction. The 95% confidence intervals for the coupling modifiers are –1.4 < κ λ < 6.9 and –0.5 < κ 2V < 2.7, assuming all other Higgs boson couplings except the one under study are fixed to the Standard Model predictions. The results are interpreted in the Standard Model effective field theory and Higgs effective field theory frameworks in terms of constraints on the couplings of anomalous Higgs boson (self-)interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

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↗

NASA NTRS2009

The Astrophysics Science Division Annual Report 2008

The Astrophysics Science Division (ASD) at Goddard Space Flight Center (GSFC) is one of the largest and most diverse astrophysical organizations in the world, with activities spanning a broad range of topics in theory, observation, and mission and technology development. Scientific research is carried out over the entire electromagnetic spectrum from gamma rays to radio wavelengths as well as particle physics and gravitational radiation. Members of ASD also provide the scientific operations for three orbiting astrophysics missions WMAP, RXTE, and Swift, as well as the Science Support Center for the Fermi Gamma-ray Space Telescope. A number of key technologies for future missions are also under development in the Division, including X-ray mirrors, and new detectors operating at gamma-ray, X-ray, ultraviolet, infrared, and radio wavelengths. This report includes the Division's activities during 2008.

Oegerle, William↗