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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Quaternary Semiconductor Cd1−xZnxTe1−ySey for High-Resolution, Room-Temperature Gamma-Ray Detection

The application of Cd0.9Zn0.1Te (CZT) single crystals, the primary choice for high-resolution, room-temperature compact gamma-ray detectors in the field of medical imaging and homeland security for the past three decades, is limited by the high cost of production and maintenance due to low detector grade crystal growth yield. The recent advent of its quaternary successor, Cd0.9Zn0.1Te1−ySey (CZTS), has exhibited remarkable crystal growth yield above 90% compared to that of ~33% for CZT. The inclusion of Se in appropriate stoichiometry in the CZT matrix is responsible for reducing the concentration of sub-grain boundary (SGB) networks which greatly enhances the compositional homogeneity and growth yield. SGB networks also host defect centers responsible for charge trapping, hence their reduced concentration ensures minimized charge trapping. Indeed, CZTS single crystals have shown remarkable improvement in electron charge transport properties and energy resolution over CZT detectors. However, our studies have found that the overall charge transport in CZTS is still limited by the hole trapping. In this article, we systematically review the advances in the CZTS growth techniques, its performance as room-temperature radiation detector, and the role of defects and their passivation studies needed to improve the performance of CZTS detectors further.

36 MATERIALS SCIENCE↗
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 ↗
NASA NTRS2016

Prospects for Observing and Localizing Gravitational-Wave Transients with Advanced LIGO and Advanced Virgo

We present a possible observing scenario for the Advanced LIGO and Advanced Virgo gravitational-wave detectors over the next decade, with the intention of providing information to the astronomy community to facilitate planning for multi-messenger astronomy with gravitational waves. We determine the expected sensitivity of the network to transient gravitational-wave signals, and study the capability of the network to determine the sky location of the source. We report our findings for gravitational-wave transients, with particular focus on gravitational-wave signals from the inspiral of binary neutron-star systems, which are considered the most promising for multi-messenger astronomy. The ability to localize the sources of the detected signals depends on the geographical distribution of the detectors and their relative sensitivity, and 90% credible regions can be as large as thousands of square degrees when only two sensitive detectors are operational. Determining the sky position of a significant fraction of detected signals to areas of 5 sq. deg to 20 sq. deg will require at least three detectors of sensitivity within a factor of approximately 2 of each other and with a broad frequency bandwidth. Should the third LIGO detector be relocated to India as expected, a significant fraction of gravitational-wave signals will be localized to a few square degrees by gravitational-wave observations alone.

counterparts↗
DOE OSTI2022

Extracting low energy signals from raw LArTPC waveforms using deep learning techniques — A proof of concept

Here, we investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid argon time projection chamber (LArTPC) detectors. A minimal generic LArTPC detector model is developed to generate realistic noise and signal waveforms used to train and test the 1D-CNN, and evaluate its performance on low-level signals. We demonstrate that our approach overcomes the inherent shortcomings of traditional cut-based methods by extending sensitivity to signals with ADC values below their imposed thresholds. This approach exhibits great promise in enhancing the capabilities of future generation neutrino experiments like DUNE to carry out their low-energy neutrino physics programs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Graph Neural Networks for low-energy event classification & reconstruction in IceCube

IceCube, a cubic-kilometer array of optical sensors built to detect atmospheric and astrophysical neutrinos between 1 GeV and 1 PeV, is deployed 1.45 km to 2.45 km below the surface of the ice sheet at the South Pole. The classification and reconstruction of events from the in-ice detectors play a central role in the analysis of data from IceCube. Reconstructing and classifying events is a challenge due to the irregular detector geometry, inhomogeneous scattering and absorption of light in the ice and, below 100 GeV, the relatively low number of signal photons produced per event. To address this challenge, it is possible to represent IceCube events as point cloud graphs and use a Graph Neural Network (GNN) as the classification and reconstruction method. The GNN is capable of distinguishing neutrino events from cosmic-ray backgrounds, classifying different neutrino event types, and reconstructing the deposited energy, direction and interaction vertex. Based on simulation, we provide a comparison in the 1 GeV–100 GeV energy range to the current state-of-the-art maximum likelihood techniques used in current IceCube analyses, including the effects of known systematic uncertainties. For neutrino event classification, the GNN increases the signal efficiency by 18% at a fixed background rate, compared to current IceCube methods. Alternatively, the GNN offers a reduction of the background (i.e. false positive) rate by over a factor 8 (to below half a percent) at a fixed signal efficiency. For the reconstruction of energy, direction, and interaction vertex, the resolution improves by an average of 13%–20% compared to current maximum likelihood techniques in the energy range of 1 GeV–30 GeV. The GNN, when run on a GPU, is capable of processing IceCube events at a rate nearly double of the median IceCube trigger rate of 2.7 kHz, which opens the possibility of using low energy neutrinos in online searches for transient events.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

The optimal use of segmentation for sampling calorimeters

One of the key design choices of any sampling calorimeter is how fine to make the longitudinal and transverse segmentation. Here, to inform this choice, we study the impact of calorimeter segmentation on energy reconstruction. To ensure that the trends are due entirely to hardware and not to a sub-optimal use of segmentation, we deploy deep neural networks to perform the reconstruction. These networks make use of all available information by representing the calorimeter as a point cloud. To demonstrate our approach, we simulate a detector similar to the forward calorimeter system intended for use in the ePIC detector, which will operate at the upcoming Electron Ion Collider. We find that for the energy estimation of isolated charged pion showers, relatively fine longitudinal segmentation is key to achieving an energy resolution that is better than 10% across the full phase space. These results provide a valuable benchmark for ongoing EIC detector optimizations and may also inform future studies involving high-granularity calorimeters in other experiments at various facilities.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Search for narrow resonances in the b -tagged dijet mass spectrum in proton-proton collisions at s = 13 TeV

A search is performed for narrow resonances decaying to final states of two jets, with at least one jet originating from a b quark, in proton-proton collisions at s = 13 TeV . The data set corresponds to an integrated luminosity of 138 fb - 1 collected with the CMS detector at the LHC. Jets originating from energetic b hadrons are identified through a b -tagging algorithm that utilizes a deep neural network or the presence of a muon inside a jet. The invariant mass spectrum of jet pairs is well described by a smooth parametrization and no evidence for the production of new particles is observed. Upper limits on the production cross section are set for excited b quarks and other resonances decaying to dijet final states containing b quarks. These limits exclude at 95% confidence level models of Z ' bosons with masses from 1.8 TeV to 2.4 TeV and of excited b quarks with masses from 1.8 TeV to 4.0 TeV. This is the most stringent exclusion of excited b quarks to date.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Gravitational Waves and Proton Decay: Complementary Windows into Grand Unified Theories

Proton decay is a smoking gun signature of grand unified theories (GUTs). Searches by Super-Kamiokande have resulted in stringent limits on the GUT symmetry-breaking scale. The large-scale multipurpose neutrino experiments DUNE, Hyper-Kamiokande, and JUNO will either discover proton decay or further push the symmetry-breaking scale above 10 16 GeV. Another possible observational consequence of GUTs is the formation of a cosmic string network produced during the breaking of the GUT to the standard model gauge group. The evolution of such a string network in the expanding Universe produces a stochastic background of gravitational waves which will be tested by a number of gravitational wave detectors over a wide frequency range. We demonstrate the nontrivial complementarity between the observation of proton decay and gravitational waves produced from cosmic strings in determining SO(10) GUT-breaking chains. We show that such observations could exclude SO(10) breaking via flipped SU(5) × U(1) or standard SU(5), while breaking via a Pati-Salam intermediate symmetry, or standard SU(5) × U(1), may be favored if a large separation of energy scales associated with proton decay and cosmic strings is indicated. We note that recent results by the NANOGrav experiment have been interpreted as evidence for cosmic strings at a scale of ~10 14 GeV. This would strongly point toward the existence of GUTs, with SO(10) being the prime candidate. We show that the combination with already available constraints from proton decay allows us to identify preferred symmetry-breaking routes to the standard model.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1978

System and method for tracking a signal source

A system for tracking moving signal sources is disclosed which is particularly adaptable for use in tracking stage performers. A miniature transmitter is attached to the person or object to be tracked and emits a detectable signal of a predetermined frequency. A plurality of detectors positioned in a preset pattern sense the signal and supply output information to a phase detector which applies signals representing the angular orientation of the transmitter to a computer. The computer provides command signals to a servo network which drives a device such as a motor driven mirror reflecting the beam of a spotlight, to track the moving transmitter.

Mogavero, L. N.↗
NASA NTRS1980

Input signal conditioner for the multimegasymbol telemetry system feasibility model

The input signal conditioner portion of the multimegasymbol telemetry demodulator and detector feasibility model is described. The input signal conditioner accepts the 55 MHz wideband IF signal of a Block 4 Deep Space Network receiver and provides selectable bandpass filtering, total power automatic gain control, and frequency translation providing inphase and quadrature outputs at baseband.

Stevens, G. L.↗
NASA NTRS2021

The spatial distribution of ionospheric threats to WAAS integrity, 2000 – 2019: a systematic analysis

The United States’ Wide Area Augmentation System (WAAS) broadcasts data to facilitate aircraft navigation. This paper examines the spatial dependence of ionospheric disturbances that have threatened the accuracy and reliability of position estimates derived from these data over the period 2000 – 2019. We address two distinct aspects of this spatial dependence: (1) the geographic distribution of these threats, in particular, in relation to geomagnetic latitude, and (2) the geometric dependence of threats relative to the locations of the receiver sites that comprise the WAAS network. We analyze threat distributions in terms of the various means that WAAS employs to mitigate these threats, including the Extreme Storm Detector, the Moderate Storm Detector, local irregularity detectors, and the ionospheric threat model. Distinct distributions are presented for threats occurring in Solar Cycle 23 and those of Solar Cycle 24. To study the geometric dependence of threats on receiver locations, we use as a metric the distance separating a threat from the centroid of the nearest Ncentroid receivers. Large values of this metric identify threats at or beyond the edge of coverage. We conclude by discussing the implications of our results for WAAS operations.

Altshuler, Eric↗
NASA NTRS2023

Airborne Lightning Observatory for FEGS and TGFs (ALOFT)

The ALOFT campaign is a unique suborbital campaign to advance the science of highenergy radiation emissions from thunderstorms, validate existing spaceborne lightning mappers and evaluate design concepts for next-generation mappers, and study convection from a suborbital platform. ALOFT is a collaboration between NASA, the University of Bergen, and other institutions that will fly the ER-2 aircraft over tropical thunderstorms around the Gulf of Mexico, Central America, and the Caribbean. The payload will consist of lightning detectors, gamma-ray scintillators, and a mixture of passive and/or active microwave sensors. Supporting the flights will be a diverse groundbased network of lightning instruments spread across the region of interest.

Nikolai Ostgaard↗
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 OSTI2023

Deep Learning Approach for High-accuracy Electron Counting of Monolithic Active Pixel Sensor-type Direct Electron Detectors at Increased Electron Dose

Abstract Electron counting can be performed algorithmically for monolithic active pixel sensor direct electron detectors to eliminate readout noise and Landau noise arising from the variability in the amount of deposited energy for each electron. Errors in existing counting algorithms include mistakenly counting a multielectron strike as a single electron event, and inaccurately locating the incident position of the electron due to lateral spread of deposited energy and dark noise. Here, we report a supervised deep learning (DL) approach based on Faster region-based convolutional neural network (R-CNN) to recognize single electron events at varying electron doses and voltages. The DL approach shows high accuracy according to the near-ideal modulation transfer function (MTF) and detector quantum efficiency for sparse images. It predicts, on average, 0.47 pixel deviation from the incident positions for 200 kV electrons versus 0.59 pixel using the conventional counting method. The DL approach also shows better robustness against coincidence loss as the electron dose increases, maintaining the MTF at half Nyquist frequency above 0.83 as the electron density increases to 0.06 e−/pixel. Thus, the DL model extends the advantages of counting analysis to higher dose rates than conventional methods.

Materials Science↗
DOE OSTI2025

Machine learning for single-ended event reconstruction in PROSPECT experiment

The Precision Reactor Oscillation and Spectrum Experiment, PROSPECT, was a segmented antineutrino detector that successfully operated at the High Flux Isotope Reactor in Oak Ridge, TN, during its 2018 run. Despite challenges with photomultiplier tube base failures affecting some segments, innovative machine learning approaches were employed to perform position and energy reconstruction, and particle classification. This work highlights the effectiveness of convolutional neural networks and graph convolutional networks in enhancing data analysis. By leveraging these techniques, a 3.3% increase in effective statistics was achieved compared to traditional methods, showcasing their potential to improve analysis performance. Furthermore, these machine learning methodologies offer promising applications for other segmented particle detectors, underscoring their versatility and impact.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Nonlocal subpicosecond delay metrology using spectral quantum interference

Precise knowledge of position and timing information is critical to support elementary protocols such as entanglement swapping on quantum networks. While approaches have been devised to use quantum light for such metrology, they largely rely on time-of-flight (ToF) measurements with single-photon detectors and, therefore, are limited to picosecond-scale resolution owing to detector jitter. In this work, we demonstrate an approach to distributed sensing that leverages phase modulation to map changes in the spectral phase to coincidence probability, thereby overcoming the limits imposed by single-photon detection. By extracting information about the joint biphoton phase, we measure a generalized delay—the difference in signal–idler arrival, relative to local radio frequency (RF) phase modulation. For nonlocal ranging measurements, we achieve (2$σ$) precision of ±0.04 ps and for measurements of the relative RF phase, (2$σ$) precision of ±0.7°. We complement this fine timing information with ToF data from single-photon time-tagging to demonstrate absolute measurement of time delay. By relying on off-the-shelf telecommunications equipment and standard quantum resources, this approach has the potential to reduce overhead in practical quantum networks.

42 ENGINEERING↗
DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗
NASA NTRS1994

Learning random networks for compression of still and moving images

Image compression for both still and moving images is an extremely important area of investigation, with numerous applications to videoconferencing, interactive education, home entertainment, and potential applications to earth observations, medical imaging, digital libraries, and many other areas. We describe work on a neural network methodology to compress/decompress still and moving images. We use the 'point-process' type neural network model which is closer to biophysical reality than standard models, and yet is mathematically much more tractable. We currently achieve compression ratios of the order of 120:1 for moving grey-level images, based on a combination of motion detection and compression. The observed signal-to-noise ratio varies from values above 25 to more than 35. The method is computationally fast so that compression and decompression can be carried out in real-time. It uses the adaptive capabilities of a set of neural networks so as to select varying compression ratios in real-time as a function of quality achieved. It also uses a motion detector which will avoid retransmitting portions of the image which have varied little from the previous frame. Further improvements can be achieved by using on-line learning during compression, and by appropriate compensation of nonlinearities in the compression/decompression scheme. We expect to go well beyond the 250:1 compression level for color images with good quality levels.

Gelenbe, Erol↗