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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Fermi-LAT Gamma-Ray Emission Discovered from the Composite Supernova Remnant B0453-685 in the Large Magellanic Cloud

Abstract We report the second extragalactic pulsar wind nebula (PWN) to be detected in the megaelectronvolt–gigaelectronvolt band by the Fermi-LAT, located within the Large Magellanic Cloud. The only other known PWN to emit in the Fermi band outside of the Milky Way is N157B, which lies to the west of the newly detected gamma-ray emission at an angular distance of 4°. Faint, pointlike gamma-ray emission is discovered at the location of the composite supernova remnant (SNR) B0453-685 with a ∼4 σ significance with energies ranging from 300 MeV–2 TeV. We present the Fermi-LAT data analysis of the new gamma-ray source, coupled with a detailed multiwavelength investigation to understand the nature of the observed emission. Combining the observed characteristics of the SNR and the physical implications from broadband modeling, we argue it is unlikely that the SNR is responsible for the gamma-ray emission. While the gamma-ray emission is too faint for a pulsation search, we try to distinguish between any pulsar and PWN component of SNR B0453-685 that could be responsible for the observed gamma-ray emission using semi-analytic models. We determine the most likely scenario is that the old PWN ( τ ∼ 14,000 yr) within B0453-685 has been impacted by the return of the SNR reverse shock with a possible substantial pulsar component below 5 GeV.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Enhanced detection of paramagnetic fluorine-19 magnetic resonance imaging agents using zero echo time sequence and compressed sensing

Fluorine-19 ( 19 F) magnetic resonance imaging (MRI) is an emerging technique offering specific detection of labeled cells in vivo. Lengthy acquisition times and modest signal-to-noise ratio (SNR) makes three-dimensional spin-density–weighted 19 F imaging challenging. Recent advances in tracer paramagnetic metallo-perfluorocarbon (MPFC) nanoemulsion probes have shown multifold SNR improvements due to an accelerated 19 F T 1 relaxation rate and a commensurate gain in imaging speed and averages. However, 19 F T 2 -reduction and increased linewidth limit the amount of metal additive in MPFC probes, thus constraining the ultimate SNR. To overcome these barriers, we describe a compressed sampling (CS) scheme, implemented using a “zero” echo time (ZTE) sequence, with data reconstructed via a sparsity-promoting algorithm. Our CS-ZTE scheme acquires k-space data using an undersampled spherical radial pattern and signal averaging. Image reconstruction employs off-the-shelf sparse solvers to solve a joint total variation and -norm regularized least square problem. To evaluate CS-ZTE, we performed simulations and acquired 19 F MRI data at 11.7 T in phantoms and mice receiving MPFC-labeled dendritic cells. For MPFC-labeled cells in vivo, we show SNR gains of ~6.3 × with 8-fold undersampling. We show that this enhancement is due to three mechanisms including undersampling and commensurate increase in signal averaging in a fixed scan time, denoising attributes from the CS algorithm, and paramagnetic reduction of T 1 . Importantly, 19 F image intensity analyses yield accurate estimates of absolute quantification of 19 F spins. Overall, the CS-ZTE method using MPFC probes achieves ultrafast imaging, a substantial boost in detection sensitivity, accurate 19 F spin quantification, and minimal image artifacts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Analytical estimation of the signal to noise ratio efficiency in axion dark matter searches using a Savitzky-Golay filter

The signal to noise ratio efficiency ϵ SNR in axion dark matter searches has been estimated using large-statistic simulation data reflecting the background information and the expected axion signal power obtained from a real experiment. This usually requires a lot of computing time even with the assistance of powerful computing resources. Employing a Savitzky-Golay filter for background subtraction, in this work, we estimated a fully analytical ϵ SNR without relying on large-statistic simulation data, but only with an arbitrary axion mass and the relevant signal shape information. Hence, our work can provide ϵ SNR using minimal computing time and resources prior to the acquisition of experimental data, without the detailed information that has to be obtained from real experiments. Axion haloscope searches have been observing the coincidence that the frequency independent scale factor ξ is approximately consistent with the ϵ SNR . This was confirmed analytically in this work, when the window length of the Savitzky-Golay filter is reasonably wide enough, i.e., at least 5 times the signal window.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

DESPERATE: A Python Library for Processing and Denoising NMR Spectra

NMR spectroscopy is an inherently insensitive technique with respect to the amount of observable signal. A common element in all NMR spectra is random thermal noise that is often characterized by a signal-to-noise ratio (SNR). SNR can be generically improved experimentally with repetitive signal averaging or during post-processing with apodization; the former of which often results in long experimental times and the latter results in the loss of spectral resolution. Denoising techniques can instead be used during post-processing to enhance SNR without compromising resolution. The most common approach relies on the singular-value decomposition (SVD) to discard noisy components of NMR data. SVD-based approaches work well, such as Cadzow and PCA, but are computationally expensive when used for large datasets that are often encountered in NMR (e.g., Carr-Purcell/Meiboom-Gill and nD datasets). Herein, we describe the implementation of a new wavelet transform (WT) routine for the fast and robust denoising of 1D and 2D NMR spectra. Several simulated and experimental datasets are denoised with both SVD-based Cadzow or PCA and WT’s, and the resulting SNR enhancements and spectral uniformity are compared. WT denoising offers similar and improved denoising compared with SVD and operates faster by several orders-of-magnitude in some cases. Further, all denoising and processing routines used in this work are included in a free and open-source Python library called DESPERATE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

A source of gamma rays coincident with the shell of the supernova remnant CTB 80

ABSTRACT CTB 80 (G69.0+2.7) is a relatively old (50–80 kyr) supernova remnant (SNR) with a complex radio morphology showing three extended radio arms and a radio and X-ray nebula near the location of the pulsar PSR B1951+32. We report on a study of the GeV emission in the region of CTB 80 with Fermi-Large Area Telescope data. An extended source with a size of 1.3°, matching the size of the infrared shell associated to the SNR, was discovered. The GeV emission, detected up to an energy of ∼20 GeV, is more significant at the location of the northern radio arm where previous observations imply that the SNR shock is interacting with ambient material. Both hadronic and leptonic scenarios can reproduce the multiwavelength data reasonably well. The hadronic cosmic ray energy density required is considerably larger than the local Galactic value and the gamma-ray leptonic emission is mainly due to bremsstrahlung interactions. We conclude that GeV particles are still trapped or accelerated by the SNR producing the observed high-energy emission when interacting with ambient material.

Araya, M.↗
DOE OSTI2021

Statistical and systematic uncertainties in extracting the source properties of neutron star-black hole binaries with gravitational waves

Gravitational waves emitted by neutron star black hole mergers encode key properties of neutron stars - such as their size, maximum mass and spins - and black holes. However, the presence of matter and the high mass ratio makes generating long and accurate waveforms from these systems hard to do with numerical relativity, and not much is known about systematic uncertainties due to waveform modeling. In this work, we simulate gravitational waves from neutron star black hole mergers by hybridizing numerical relativity waveforms produced with the SpEC code with a recent numerical relativity surrogate NRHybSur3dq8Tidal. These signals are analyzed using a range of available waveform families, and statistical and systematic errors are reported. We find that at a network signal-to-noise ratio (SNR) of 30, statistical uncertainties are usually larger than systematic offsets, while at an SNR of 70 the two become comparable. The individual black hole and neutron star masses, as well as the mass ratios, are typically measured very precisely, though not always accurately at high SNR. At a SNR of 30 the neutron star tidal deformability can only be bound from above, while for louder sources it can be measured and constrained away from zero. All neutron stars in our simulations are non-spinning, but in no case we can constrain the neutron star spin to be smaller than ~0.4 (90% credible interval). Waveform families whose late inspiral has been tuned specifically for neutron star black hole signals typically yield the most accurate characterization of the source parameters. Their measurements are in tension with those obtained using waveform families tuned against binary neutron stars, even for mass ratios that could be relevant for both binary neutron stars and neutron star black holes mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Calibrated Radar Wind Profiler (RWP) Moments

The SGP Central Facility (C1) radar wind profiler (RWP) was calibrated using nearby surface disdrometer observations. Between 2011 and 2019, the SGP C1 RWP operated in two modes. The vertically pointing mode (named the precipitation mode) transmitted a short and long pulse length to have two different range resolutions and the beam-swinging mode (named the wind mode) transmitted one pulse length into three different beam directions. The precipitation-mode observations were available and calibrated from April 2011 through mid-August 2019. The wind-mode observations were available and calibrated between April 2014 and March 2019. The RWP spectra were processed to account for Nyquist velocity aliasing and coherent integration filtering effects before calculating the spectrum moments. During intense precipitation events, the calculated signal-to-noise ratio (SNR) is biased low due to signal power being distributed across the velocity spectrum such that some signal power is erroneously included in the noise level estimate, causing the noise level to be biased high. To correct for the low SNR bias, a new noise level is estimated using observations without precipitation and the SNR is increased accordingly. The adjusted SNR was converted to radar reflectivity factor and then calibrated against a nearby surface disdrometer. The calibration methodology is fully described in: Williams, CR, J Barrio, PE Johnston, P Muradyan, and S Giangrande. 2023. “Calibrating radar wind profiler reflectivity factor using surface disdrometer observations.” Atmospheric Measurement Techniques, https://egusphere.copernicus.org/preprints/2023/egusphere-2022-1405/

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Remote Sensing Low Signal-to-Noise-Ratio Target Detection Enhancement

In real-time remote sensing application, frames of data are continuously flowing into the processing system. The capability of detecting objects of interest and tracking them as they move is crucial to many critical surveillance and monitoring missions. Detecting small objects using remote sensors is an ongoing, challenging problem. Since object(s) are located far away from the sensor, the target’s Signal-to-Noise-Ratio (SNR) is low. The Limit of Detection (LOD) for remote sensors is bounded by what is observable on each image frame. In this paper, we present a new method, a “Multi-frame Moving Object Detection System (MMODS)”, to detect small, low SNR objects that are beyond what a human can observe in a single video frame. This is demonstrated by using simulated data where our technology-detected objects are as small as one pixel with a targeted SNR, close to 1:1. We also demonstrate a similar improvement using live data collected with a remote camera. The MMODS technology fills a major technology gap in remote sensing surveillance applications for small target detection. Our method does not require prior knowledge about the environment, pre-labeled targets, or training data to effectively detect and track slow- and fast-moving targets, regardless of the size or the distance.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Design and characterization of a single-channel microwave interferometer for the Helicon Physics Prototype eXperiment

A heterodyne interferometer for measuring the plasma electron density in Helicon Physics Prototype eXperiment (HPPX) has been developed and characterized in the laboratory. The heterodyne-type interferometer uses a probing beam of 85 GHz and a reference beam of 85.7 GHz by using two Gunn oscillators for facilitating frequency modulation. The cutoff density for this frequency is 8.96 x 10 19 m –3 that is much higher than the nominal electron density of the HPPX plasmas. The maximum deflection angle due to the refraction effect is estimated to be 0.63° assuming a parabolic electron density profile, and the impact on the measurement is negligible for the plasmas with a size of 100 mm. Two identical HDPE lenses act as the vacuum windows, and the lenses working together with the transmitter and receiver horns, form a beam with the beam waist of about 20 mm located at the plasma center. The characterization results of the lens and the critical microwave components show good performance. The plasma electron density measurement results are in line with the expectation. Furthermore, the Signal to Noise Ratio (SNR) of the measurement is defined as SNR = 20lg($\bar{n}_{e}$/S), where $\bar{n}_{e}$ and S are respectively the average electron density and the standard deviation of the signal, and the SNR is 80 dB. Finally, this demonstrates the system's capability of providing a clear and detailed density resolution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Convergent laser beam shapes: Unveiling the dynamics of Laser-induced elastic waves in composite materials

Overcoming the low signal-to-noise ratio (SNR) in laser ultrasonic testing of composite materials remains a significant challenge. Current efforts focus on enhancing SNR by inserting more energy into the material through temporal and/or spatial modulation of the laser beam. However, potential SNR improvements through wave convergence and wave energy manipulation have been overlooked. This paper addresses this gap by demonstrating the convergence of different wave types to a designated point and by showing the feasibility of directing absorbed laser energy into a specific wave type through spatial modulation of the laser beam. To achieve this, mathematical expressions for the convergent laser beams are derived. Various laser beam profiles are then introduced to the thermoelastic equations and solved using the finite element method. The sample under investigation is a transversely isotropic unidirectional carbon fiber reinforced plastic, characterized by anisotropic thermal expansion coefficients and thermal conductivities. Results reveal pronounced convergence of the intended wave type at the center due to laser beam shaping. This study showcases the ability to direct absorbed laser energy toward a specific wave type through spatial modulation of the laser beam and highlights the role of material anisotropy in energy focusing.

composite materials↗
DOE OSTI2023

Exploring Mission Design for Imaging Spectroscopy Retrievals for Land and Aquatic Ecosystems

The retrieval algorithms used for optical remote sensing satellite data to estimate Earth's geophysical properties have specific requirements for spatial resolution, temporal revisit, spectral range and resolution, and instrument signal-to-noise ratio (SNR) performance to meet biogeoscience objectives. Studies to estimate surface properties from hyperspectral data use a range of algorithms sensitive to various sources of spectroscopic uncertainty, which are in turn influenced by mission architecture choices. Retrieval algorithms vary across scientific fields and may be more or less sensitive to mission architecture choices that affect spectral, spatial, or temporal resolutions and spectrometer SNR. We used representative remote sensing algorithms across terrestrial and aquatic study domains to inform aspects of mission design that are most important for impacting accuracy in each scientific area. We simulated the propagation of uncertainties in the retrieval process including the effects of different instrument configuration choices. We found that retrieval accuracy and information content degrade consistently at >10 nm spectral resolution, >30 m spatial resolution, and >8-day revisit. In these studies, the noise reduction associated with lower spatial resolution improved accuracy vis à vis high spatial resolution measurements. The interplay between spatial resolution, temporal revisit, and SNR can be quantitatively assessed for imaging spectroscopy missions and used to identify key components of algorithm performance and mission observing criteria.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Implementation of compound refractive lenses for large field-of-view x-ray phase-contrast imaging during hypervelocity impact experiments

Synchrotron x-ray phase-contrast imaging (XPCI) offers time-resolved visualization of dynamic compression phenomena, but its intrinsically small field-of-view (FOV) limits the time that key features remain in frame. A novel approach to enlarge the FOV is achieved by positioning a two-dimensional parabolic compound refractive lens (CRL) upstream of the sample to deliberately defocus the white beam. Ray-tracing simulations and XPCI measurements show that this CRL configuration can expand the beam by ∼50% vertically and ∼15% horizontally based on the full width at half-maximum of the beam. Implementing the CRL, however, attenuates the photon flux and lowers signal-to-noise ratio (SNR). Task-based analysis using a calibration grid (30 μm dots) showed that both setups fail to consistently meet the Rose criterion (SNR ≥ 5) for features of this size in single-bunch imaging. Extrapolating the measured SNR Rose values suggests that the minimum consistently detectable feature lies closer to 30–40 μm for the standard XPCI setup and above 40 μm for CRL-XPCI. Despite this limitation, the CRL configuration nearly doubles the illuminated area, enabling simultaneous tracking of front and rear observations of boron carbide targets subjected to rod and sphere impacts at 1.0–2.6 km/s. Image tracking algorithms and photonic Doppler velocimetry were used to measure penetration and rear-surface velocity histories. Together, these measurements capture crack fronts, penetration, and material breakout, offering new benchmark data for validating high-strain-rate constitutive models of ceramic materials.

Ceramic materials↗
DOE OSTI2020

Development of an MEMS ultrasonic microphone array system and its application to compressed wavefield imaging of concrete

Abstract Although contactless ultrasonic wavefield imaging shows potential for effective nondestructive inspection of various engineering materials, it has been rarely applied to concrete materials owing to technical challenges including low signal-to-noise ratio (SNR) caused by inherent heterogeneity of concrete. This paper presents development of a multi-channel MEMS ultrasonic microphone array system and its application to compressed wavefield imaging of concrete materials. The developed multi-channel MEMS ultrasonic microphone array system contains eight MEMS ultrasonic microphone elements and a signal conditioning circuit that enables measurements of ultrasonic signals with high SNR. A compressed sensing approach, based on the multiple measurement vector (MMV) concept, is applied to reconstruct a full dense ultrasonic wavefield data from sparsely sampled ultrasonic wavefield data. Experiments are carried out on a laboratory concrete sample to verify the performance of the developed MEMS microphone array system and proposed compressed sensing approach and then large-scale concrete samples to demonstrate practical application. The experimental results demonstrate that the developed MEMS microphone array system provides high-quality (SNR > 20 dB) ultrasonic data collected from concrete elements; furthermore, the proposed compressed sensing approach provides accurate reconstruction of dense wavefield data, as determined by peak signal-to-noise ratio (PSNR), from sparsely measured wavefield data with compression ratios up to 85% and PSNR above 25 dB in data collected form realistic large-scale concrete samples. By combining the MEMS array system and compressed sensing approach, the total ultrasonic data acquisition time needed to produce dense wavefield data can be significantly reduced.

Instruments & Instrumentation↗
DOE OSTI2025

Evaluation of a generalized least squares algorithm for infrasound beamforming with coherent background noise

Infrasonic signals of interest can occur during periods with persistent, coherent, background noise, which may be natural or anthropogenic. For high signal-to-noise (SNR) ratio transient signals, an ‘overprinting’ of the coherent background may occur, and the signal may still be detected. However, this approach fails for low SNR signals of interest, which may be obscured by coherent noise. An infrasound beamforming method based on generalized least squares (GLS) is investigated for detecting transient signals of interest in the presence of coherent and incoherent background noise. This approach relies on an estimate of the noise covariance, captured in a covariance matrix, to effectively null contributions to the array response from noisy directions of arrival. Synthetic array data is used to investigate the performance of the GLS beamformer compared to the Bartlett beamformer when coherent and incoherent backgrounds are present. Additionally, the effects of array element number and relative strength of the interfering signal on the GLS estimates is investigated. GLS empirical area under the curve estimates suggest that the beamformer can recover coherent power for a signal of interest lower in amplitude than the coherent background, but this effectiveness degrades more quickly with SNR for a four element array compared to a six or eight element infrasound array. Finally, infrasound from the Forensic Surface Experiment, a bolide signal observed at IMS array I37NO, and a volcanic signal recorded at the Alaska Volcano Observatory array ADKI are used to evaluate GLS performance on recorded data. A ten minute window was used to capture the background noise, and the coherent background signal was nulled in all three examples.

58 GEOSCIENCES↗
DOE OSTI2025

Forecasting the Ly-𝛼 forest × CMB lensing bispectrum from ACT, SO, S4, and DESI

We forecast the sensitivity of future correlations between cosmic microwave background (CMB) lensing and the Lyman-𝛼 forest power spectrum. This squeezed-limit bispectrum probes the connection between the Lyman-𝛼 transmission and the underlying large-scale matter density field. We recover the measured signal-to-noise (SNR) ratio obtained with Planck × BOSS, and forecast a SNR of 10.3, 14.8 and 20.2 for DESI combined with ACT, SO, and CMB-S4, respectively. For DESI and SO/CMB-S4, the correlation should be detectable at SNR ≥ 5 in 5 redshift bins, useful for distinguishing our signal from several contaminants. Indeed, our forecast is affected by large theoretical uncertainties in the current modeling of the signal and its contaminants, such as continuum misestimation bias or damped-Lyman-𝛼 absorbers. Quantifying these more accurately will be crucial to enable a reliable cosmological interpretation of this observable in future measurements. We attempt to enumerate the features required in future Lyman-𝛼 forest simulations required to do so.

Cosmic microwave background↗
DOE OSTI2024

Maximizing the Radar Generalized Image Quality Equation for Bistatic SAR Using Waveform Frequency Agility

The radar generalized image quality equation (RGIQE) is a metric used to measure both monostatic and bistatic synthetic aperture radar (BSAR) image quality, it is a function of signal-to-noise ratio (SNR) and 2-D bandwidth. The 2-D bandwidth is equal to the area of the transfer function’s (TF) passband region. With the exception of side-looking monostatic geometries, almost all monostatic and bistatic geometries have skewed passband shapes when waveform frequency parameters remain unchanged from pulse to pulse. Most synthetic aperture radar (SAR) applications require a rectangular-shaped passband region, this is achieved by inscribing a rectangular region within the skewed intrinsic passband region. Increasing skewness results in less inscription area reducing 2-D bandwidth, image SNR, and thus RGIQE capacity. In this article, a waveform with frequency agility is used to rectify the skewness that degrades RGIQE capacity. By changing the waveform’s center frequency and instantaneous bandwidth from pulse to pulse in a particular manner, the intrinsic passband region can be de-skewed. The de-skewed shape maximizes the inscription area thus maximizing 2-D bandwidth, image SNR, and RGIQE capacity. Here, three examples are given in this article, one monostatic geometry, and two bistatic geometries. RGIQE capacity is increased by 52.02%, 44.42%, and 79.09% for the three examples.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Real-time image denoising of mixed Poisson–Gaussian noise in fluorescence microscopy images using ImageJ

Fluorescence microscopy imaging speed is fundamentally limited by the measurement signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate, computational denoising techniques can be used to suppress noise. However, common techniques to estimate a denoised image from a single frame either are computationally expensive or rely on simple noise statistical models. These models assume Poisson or Gaussian noise statistics, which are not appropriate for many fluorescence microscopy applications that contain quantum shot noise and electronic Johnson–Nyquist noise, therefore a mixture of Poisson and Gaussian noise. In this paper, we show convolutional neural networks (CNNs) trained on mixed Poisson and Gaussian noise images to overcome the limitations of existing image denoising methods. The trained CNN is presented as an open-source ImageJ plugin that performs real-time image denoising (within tens of milliseconds) with superior performance (SNR improvement) compared to conventional fluorescence microscopy denoising methods. The method is validated on external datasets with out-of-distribution noise, contrast, structure, and imaging modalities from the training data and consistently achieves high-performance ( > <#comment/> 8 d B ) denoising in less time than other fluorescence microscopy denoising methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Evaluation of Advanced Phased-Array Techniques: Interim Results [Slides]

Report: “Evaluation of Advanced Phased-Array Techniques: Interim Results”, August 2023 ML23216A009, PNNL-34622 Results showed that detection rate drops as microstructures/materials become more complex Standard PA had the highest detection rates overall Detection rates in the CASS-CASS mockups were lowest overall FMC had the most accurate length sizing in the fine-grained WSS mockups. All methods struggled with length sizing in coarse-grained mockups SNR vs method depended on the specimen type SNR was consistently high with standard PA SNR was consistently the lowest with PWI FMC performed best on fine-grained WSS A lack of data received from some Round Robin participants resulted in low statistics for many of the analyses.

42 ENGINEERING↗