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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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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

First results from the Goddard High-Resolution spectrograph - High-resolution observations of the 1942 A resonance line of HG II in the chemically peculiar B star, Chi Lupi

The Goddard High-Resolution Spectrograph on the HST has been used to obtain high S/N observations of the sharp-lined, Hg- and Pt-rich B-type star, Chi Lupi, with a resolving power of 87,000. The observations reveal a level of spectroscopic detail never before observed at ultraviolet wavelengths for any star other than the sun. Concentrating on the region around the resonance line of Hg II at 1942 A, the profile and central position of this line confirm beyond doubt that the Hg isotope anomaly in Chi Lupi is real and extreme, with Hg being heavily concentrated in the form of Hg-204. The problems in atomic physics which impair the accurate analysis of spectra of this quality are emphasized.

Leckrone, David S.↗
NASA NTRS1992

Resolution and noise properties of the Goddard High-Resolution Spectrograph

The Goddard High-Resolution Spectrograph (GHRS) observation characteristics and data analysis procedures, including deconvolution, are discussed. Particular attention is given to the detection of weak absorption lines in low-signal-to-noise observations. It is found that observations with the GHRS large aperture provide greater sensitivity for the detection of weak, isolated spectral features than does the small aperture for equal observing times. The noise characteristics are well represented by a Poisson distribution at low signal-to-noise characteristics. It is proposed that GHRS observations should be routinely structured with the use of comb-addition, quarter-substepping, FP-SPLITS, and multiple repeats to enable the proper discrimination against noise events capable of mimicking spectral features.

Gilliland, Ronald L.↗
NASA NTRS1998

Investigating the Near-Infrared Properties of Planetary Nebulae II. Medium Resolution Spectra: Medium Resolution Spectra - 2

We present medium-resolution (R approximately 700) near-infrared (lambda = 1 - 2.5 micrometers) spectra of a sample of planetary nebulae (PNe). A narrow slit was used which sampled discrete locations within the nebulae; observations were obtained at one or more positions in the 41 objects included in the survey. The PN spectra fall into one of four general categories: H1 emission line-dominated PNe, H1 and H2 emission line PNe, H2 emission line-dominated PNe, and continuum-dominated PNe. These categories correlate with morphological type, with the elliptical PNe falling into the first group, and the bipolar PNe primarily in the H2 and continuum emission groups. The categories also correlate with C/O ratio, with the O-rich objects falling into the first group and the C-rich objects in the groups. Other spectral features were observed in all catagories, such as continuum emission from the central star, and warm dust continuum emission towards the long wavelength end of the spectra. H2 was detected in four PNe in this survey for the first time. An analysis was performed using the H2 line ratios in all of the PN spectra in the survey where a sufficient number of lines were observed to determine the ortho-to-para ratio and the rotational and vibrational excitation temperatures of the H-2 in those objects. One unexpected result from this analysis is that the H-2 is excited by absorption of ultraviolet photons in most of the PNe, although there are several PNe in which collisional excitation plays an important role. The correlation between bipolar morphology and H2 emission has been strengthened with the new detections of H2 in this survey.

Hora, Joseph L.↗
NASA NTRS2006

Advanced Remote-sensing Imaging Emission Spectrometer (ARIES): AIRS Spectral Resolution with MODIS Spatial Resolution

This paper describes a space based instrument concept that will provide scientists with data needed to support key ongoing and future Earth System Science investigations. The measurement approach builds on the observations made by AIRS and MODIS and exceeds their capability with improved spatial and spectral resolution. This paper describes the expected products and the instrument concept that can meet those requirements.

spectrometers↗
DOE OSTI2024

Advances in Image-Domain Multi-Resolution and Super-Resolution Algorithms for Industrial X-Ray Computed Tomography: A Literature Survey and New Insights

Industrial X-ray Computed Tomography (XCT) is a nondestructive method for inspection and character ization of materials and parts. XCT captures images of a part from various angles, and these images are then used to construct Three-Dimensional (3D) representations of that part. This method enables assessing the quality of the parts, identifying defects and understanding their physical properties without damaging them.

36 MATERIALS SCIENCE↗
NASA NTRS1999

The BATSE Gamma-Ray Burst Spectral Catalog: High Time Resolution Spectroscopy of Bright Bursts Using High Energy Resolution Data - 1

This is the first in a series of gamma-ray burst spectroscopy catalogs from the Burst And Transient Source Experiment (BATSE) on the Compton Gamma Ray Abstract: Observatory, each covering a different aspect of burst phenomenology. In this paper, we present time-sequences of spectral fit parameters for 156 bursts selected either for their high peak flux or fluence.

Preece, Robert D.↗
DOE OSTI2024

CryoTRANS: predicting high-resolution maps of rare conformations from self-supervised trajectories in cryo-EM

Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.Cryogenic electron microscopy (cryo-EM) has revolutionized structural biology, enabling efficient determination of structures at near-atomic resolutions. However, a common challenge arises from the severe imbalance among various conformations of vitrified particles, leading to low-resolution reconstructions in rare conformations due to a lack of particle images in these quasi-stable states. We introduce CryoTRANS, a method that predicts high-resolution maps of rare conformations by constructing a self-supervised pseudo-trajectory between density maps of varying resolutions. This trajectory is represented by an ordinary differential equation parameterized by a deep neural network, ensuring retention of detailed structures from high-resolution density maps. By leveraging a single high-resolution density map, CryoTRANS significantly improves the reconstruction of rare conformations and has been validated on four real-world datasets: alpha-2-macroglobulin, actin-binding protein complexes, SARS-CoV-2 spike glycoprotein, and the 70S ribosome. CryoTRANS can also predict high-resolution structures in cryogenic electron tomography maps using a high-resolution cryo-EM map.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data

Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2011

Automated Verification of Spatial Resolution in Remotely Sensed Imagery

Image spatial resolution characteristics can vary widely among sources. In the case of aerial-based imaging systems, the image spatial resolution characteristics can even vary between acquisitions. In these systems, aircraft altitude, speed, and sensor look angle all affect image spatial resolution. Image spatial resolution needs to be verified with estimators that include the ground sample distance (GSD), the modulation transfer function (MTF), and the relative edge response (RER), all of which are key components of image quality, along with signal-to-noise ratio (SNR) and dynamic range. Knowledge of spatial resolution parameters is important to determine if features of interest are distinguishable in imagery or associated products, and to develop image restoration algorithms. An automated Spatial Resolution Verification Tool (SRVT) was developed to rapidly determine the spatial resolution characteristics of remotely sensed aerial and satellite imagery. Most current methods for assessing spatial resolution characteristics of imagery rely on pre-deployed engineered targets and are performed only at selected times within preselected scenes. The SRVT addresses these insufficiencies by finding uniform, high-contrast edges from urban scenes and then using these edges to determine standard estimators of spatial resolution, such as the MTF and the RER. The SRVT was developed using the MATLAB programming language and environment. This automated software algorithm assesses every image in an acquired data set, using edges found within each image, and in many cases eliminating the need for dedicated edge targets. The SRVT automatically identifies high-contrast, uniform edges and calculates the MTF and RER of each image, and when possible, within sections of an image, so that the variation of spatial resolution characteristics across the image can be analyzed. The automated algorithm is capable of quickly verifying the spatial resolution quality of all images within a data set, enabling the appropriate use of those images in a number of applications.

Davis, Bruce↗
DOE OSTI2025

How are Heterogeneous Nucleation Rate Observations Influenced by Instrument Resolution?

Experimental measurements of the heterogeneous nucleation rate rely on counting the number of nuclei with time. However, the size of a thermodynamically stable nucleus is often a few nanometers in diameter and is below the resolution of most (in situ) measurement techniques that provide a statistically valid sample. Due to the finite resolution of the instruments and analysis methods, it is challenging to capture the incipient nuclei and the subsequent evolution of nuclei density over time. In this work, we demonstrate the impact of instrument resolution on observed nuclei densities by comparing numerical modeling with experimental results. Further, to achieve this, we implemented heterogeneous nucleation within the pore-scale reactive transport modeling framework using classical nucleation theory (CNT). We compared the modeling results with nucleation rates measured using X-ray nanotomography (XnT) and evaluated how these impact the apparent values of the prefactor and interfacial energy based on CNT and the crystal growth rate. Specifically, we applied a resolution threshold (artificial resolution limit) in the model during nuclei counting to resemble an experimental resolution, ranging from 15 to 500 nm. The findings reveal that the instrument resolution significantly impacts the apparent prefactor and interfacial energy. Both apparent prefactor and interfacial energy decrease with a decrease in the instrument resolution. While deviation in the prefactor due to resolution is anticipated, those in the interfacial energy are unexpected. The approach described here allows one to correct apparent nucleation rates that depend on the instrument’s resolution to derive “intrinsic” CNT parameters for the prefactor and interfacial energy.

47 OTHER INSTRUMENTATION↗