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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Estimating CO 2 fluxes through integrating spatial and temporal input layers via deep learning algorithms

Background Accurate estimation of net ecosystem exchange of CO 2 fluxes (Fc) is essential for understanding carbon cycle processes and assessing ecosystem carbon budgets. However, conventional modeling approaches often emphasize temporal dynamics while overlooking the pronounced spatial heterogeneity within the footprint of eddy covariance (EC) towers, potentially limiting predictive accuracy and interpretability of Fc estimates. To address this challenge, we developed a spatiotemporal model that integrates high-resolution footprint-weighted spatial information with sequential environmental drivers. Results The integrated model combines a deeper graph convolutional network to characterize fine-scale spatial variability within EC footprints and a gated recurrent unit network to capture temporal dependencies in biophysical conditions. Using multi-year flux tower observations, remote sensing vegetation indices and footprint modeling, we evaluate the proposed method across three land cover types. This spatiotemporal model consistently outperforms temporal-only and spatial-only baselines, achieving the highest overall accuracy (R 2 = 0.9569) and the lowest RMSE (1.8128 μmol m −2 s −1 ) and MAE (1.1939 μmol m −2 s −1 ). Performance gains are particularly evident in ecosystems with strong vegetation heterogeneity, where spatial structure substantially modulates Fc variability. Conclusions This study demonstrates the importance of joint modeling spatial heterogeneity and temporal dynamics for improving Fc estimation and provides a robust method for advancing footprint-based Fc estimates across diverse ecosystems, supporting refined assessments of terrestrial carbon fluxes, and enhancing scientific foundations for carbon studies.

CO2 flux estimate↗
NASA NTRSDate not supplied

Optical Navigation Attitude Estimation and Calibration Performance Improvement using Outlier Rejection

Spacecraft optical navigation (OpNav) systems process a sequence of images of celestial bodies against a starfield background to estimate the position and velocity of the vehicle. While attitude is sometimes available from an onboard star tracker, it is often desirable to recognize the background stars in the OpNav images to better align the image. While many image processing algorithms exist for finding stars, efficiency and reliability remain key issues in the presence of extended bodies(e.g. the Moon, Earth), especially when attempting to solve the full lost-in-space problem. Some star outliers(stars identified with high residuals)could appear in the camera field of view, however using them in the attitude estimation or camera calibration would lead to less accurate results. Therefore, we require new and robust approaches to remove these outliers before any further processing. The emphasis of the work is on developing a simple and robust iterative technique to detect and reject the outliers which could be found in any frame during the lost in space attitude determination or during the camera calibration. These outliers are determined based on the residuals of the centroids of the detected stars and the corresponding location using the star catalog. If the residuals exceed a predetermined threshold value, the object will be detected as an outlier and will be removed before another attitude determination and calibration iteration is performed. The performance for both attitude determination and on-orbit camera calibration are improved by an almost two-fold increase in accuracy when applying this outlier rejection technique.

OpNav↗
DOE OSTI2026

HostSub_GP: Precise Galaxy Background Subtraction in Transient Long-slit Spectroscopy with Gaussian Processes

We present a novel host galaxy subtraction technique in long-slit spectroscopy for extragalactic transients. Unlike classic methods which generally estimate the background using simple interpolation of local galaxy flux in the 2D spectrum, our approach leverages multi-band archival images of the host galaxies to model the background emission from the galaxy in the 2D spectrum. Such imaging encodes the wavelength-dependent galaxy profile along the slit, and is readily accessible through wide-field imaging surveys. We construct a smooth prior for the 2D galaxy profile with a Gaussian process (GP) based on these reference images, and use another GP to model the correlated deviations from the prior in the observed spectrum. This enables accurate inference of the galaxy flux blended with the transient. On synthetic long-slit data of a spiral galaxy extracted from a Multi Unit Spectroscopic Explorer hyper-spectral cube, the GP method remains robust as long as the host galaxy is spatially resolved and consistently outperforms classic methods. We apply the method to archival Keck spectra of two real transients, SN 2019eix and AT 2019qiz, to further demonstrate how the method uniquely recovers weak spectral features amid strong galaxy contamination, enabling refined constraints on the properties of both transients. We have released the software implementation, HostSub_GP, a scalable toolkit that leverages JAX, with an MIT license.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2002

Focal plane infrared readout circuit with automatic background suppression

A circuit for reading out a signal from an infrared detector includes a current-mode background-signal subtracting circuit having a current memory which can be enabled to sample and store a dark level signal from the infrared detector during a calibration phase. The signal stored by the current memory is subtracted from a signal received from the infrared detector during an imaging phase. The circuit also includes a buffered direct injection input circuit and a differential voltage readout section. By performing most of the background signal estimation and subtraction in a current mode, a low gain can be provided by the buffered direct injection input circuit to keep the gain of the background signal relatively small, while a higher gain is provided by the differential voltage readout circuit. An array of such readout circuits can be used in an imager having an array of infrared detectors. The readout circuits can provide a high effective handling capacity.

Pain, Bedabrata↗
NASA NTRS1980

The contribution of young galaxies to the X-ray background

The contribution of young galaxies to the diffuse X-ray background is estimated and two mechanisms by which young galaxies may emit hard X-rays are considered. The first mechanism is direct thermal bremsstrahlung emission from a hot galactic wind powered by supernovae. Estimating mass-loss rates from galaxies by arguments based on metallicity and on the X-ray spectroscopic observations of rich clusters, it is shown that galactic winds may contribute substantially to the background. The second mechanism relies on the decreased metallicity of young galaxies to increase the number of supergiant stars and hence the numbers of hard X-ray binaries. This effect quantitatively explains the relatively large X-ray luminosity of the Magellanic clouds and also indicates that binaries in young galaxies may contribute a significant fraction of the diffuse X-ray background. Both mechanisms require that the epoch of galaxy formation be recent (redshift less than 2-3) in order to account for the observed spectral temperature (45 keV) of the hard X-ray background.

Bookbinder, J.↗
NASA NTRS2013

Bounding the Role of Black Carbon in the Climate System: a Scientific Assessment

Black carbon aerosol plays a unique and important role in Earth's climate system. Black carbon is a type of carbonaceous material with a unique combination of physical properties. This assessment provides an evaluation of black-carbon climate forcing that is comprehensive in its inclusion of all known and relevant processes and that is quantitative in providing best estimates and uncertainties of the main forcing terms: direct solar absorption; influence on liquid, mixed phase, and ice clouds; and deposition on snow and ice. These effects are calculated with climate models, but when possible, they are evaluated with both microphysical measurements and field observations. Predominant sources are combustion related, namely, fossil fuels for transportation, solid fuels for industrial and residential uses, and open burning of biomass. Total global emissions of black carbon using bottom-up inventory methods are 7500 Gg/yr in the year 2000 with an uncertainty range of 2000 to 29000. However, global atmospheric absorption attributable to black carbon is too low in many models and should be increased by a factor of almost 3. After this scaling, the best estimate for the industrial-era (1750 to 2005) direct radiative forcing of atmospheric black carbon is +0.71 W/sq m with 90% uncertainty bounds of (+0.08, +1.27)W/sq m. Total direct forcing by all black carbon sources, without subtracting the preindustrial background, is estimated as +0.88 (+0.17, +1.48) W/sq m. Direct radiative forcing alone does not capture important rapid adjustment mechanisms. A framework is described and used for quantifying climate forcings, including rapid adjustments. The best estimate of industrial-era climate forcing of black carbon through all forcing mechanisms, including clouds and cryosphere forcing, is +1.1 W/sq m with 90% uncertainty bounds of +0.17 to +2.1 W/sq m. Thus, there is a very high probability that black carbon emissions, independent of co-emitted species, have a positive forcing and warm the climate. We estimate that black carbon, with a total climate forcing of +1.1 W/sq m, is the second most important human emission in terms of its climate forcing in the present-day atmosphere; only carbon dioxide is estimated to have a greater forcing. Sources that emit black carbon also emit other short-lived species that may either cool or warm climate. Climate forcings from co-emitted species are estimated and used in the framework described herein. When the principal effects of short-lived co-emissions, including cooling agents such as sulfur dioxide, are included in net forcing, energy-related sources (fossil fuel and biofuel) have an industrial-era climate forcing of +0.22 (0.50 to +1.08) W/sq m during the first year after emission. For a few of these sources, such as diesel engines and possibly residential biofuels, warming is strong enough that eliminating all short-lived emissions from these sources would reduce net climate forcing (i.e., produce cooling). When open burning emissions, which emit high levels of organic matter, are included in the total, the best estimate of net industrial-era climate forcing by all short-lived species from black-carbon-rich sources becomes slightly negative (0.06 W/sq m with 90% uncertainty bounds of 1.45 to +1.29 W/sq m). The uncertainties in net climate forcing from black-carbon-rich sources are substantial, largely due to lack of knowledge about cloud interactions with both black carbon and co-emitted organic carbon. In prioritizing potential black-carbon mitigation actions, non-science factors, such as technical feasibility, costs, policy design, and implementation feasibility play important roles. The major sources of black carbon are presently in different stages with regard to the feasibility for near-term mitigation. This assessment, by evaluating the large number and complexity of the associated physical and radiative processes in black-carbon climate forcing, sets a baseline from which to improve future climate forcing estimates.

black carbon↗
NASA NTRS1995

Inferring the spatial and energy distribution of gamma-ray burst sources. 1: Methodology

We describe Bayesian methods for analyzing the distribution of gamma-ray burst peak photon fluxes and directions. These methods fit the differential distribution, and have the following advantages over rival methods: (1) they do not destroy information by binning or averaging the data (as do, say, chi squared, the averaged value of V/V(sub max), and angular moment analyses); (2) they straightforwardly handle uncertainties in the measured quantities; (3) they analyze the strength and direction information jointly; (4) they use information available about nondetections; and (5) they automatically identify and account for biases and selection effects given a precise description of the experiment. In these methods, the most important information needed about the instrument threshold is not its value at the times of burst triggers, as is used in the average value of V/V(sub max) analyses, but rather the value of the threshold at times when no trigger occurred. We show that this information can be summarized as an average detection efficiency that is similar to the product of the exposure and efficiency reported in the First Burst and Transient Source Experiment (BATSE) Burst (1B) Catalog, but significantly different from it at low fluxes. We also quantify an important bias that results from estimating the peak flux by scanning the burst to find the peak number of counts in a window of specified duration, as was done for the 1B Catalog. When the duration of the peak of the light curve is longer than the window duration, a simple flux estimate based on the peak counts significantly overestimates the peak flux in a nonlinear fashion that distorts the shape of the log(N)-log(P) distribution. This distortion also corrupts analyses of the V/V(sub max) distribution that use ratios of counts above background to estimate V/V(sub max). The Bayesian calculation specifies how to account for this bias. Implementation of the Bayesian approach requires some changes in the way burst data are reported that we describe in detail. Subsequent papers will report analyses of the 1B Catalog data using the methods described here.

Loredo, Thomas J.↗
DOE OSTI2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a novel NPE-based cataloging method that trains an inference network with semisynthetic astronomical images generated using PSFs and backgrounds sampled from the Sloan Digital Sky Survey. In experiments with semisynthetic images, we evaluate the method on key cataloging tasks: light source detection, star/galaxy separation, and flux measurement. A “generalist” inference network—trained with diverse PSFs and backgrounds—performs as well as a “specialist” network even when both are evaluated on the specialist’s particular PSF/background combination. This result suggests that a single NPE network can generalize across spatial variations, eliminating the need for retraining on each observational condition.

astronomy image processing↗
NASA NTRS2000

How Well Will MODIS Measure Top of Atmosphere Aerosol Direct Radiative Forcing?

The new generation of satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) will be able to detect and characterize global aerosols with an unprecedented accuracy. The question remains whether this accuracy will be sufficient to narrow the uncertainties in our estimates of aerosol radiative forcing at the top of the atmosphere. Satellite remote sensing detects aerosol optical thickness with the least amount of relative error when aerosol loading is high. Satellites are less effective when aerosol loading is low. We use the monthly mean results of two global aerosol transport models to simulate the spatial distribution of smoke aerosol in the Southern Hemisphere during the tropical biomass burning season. This spatial distribution allows us to determine that 87-94% of the smoke aerosol forcing at the top of the atmosphere occurs in grid squares with sufficient signal to noise ratio to be detectable from space. The uncertainty of quantifying the smoke aerosol forcing in the Southern Hemisphere depends on the uncertainty introduced by errors in estimating the background aerosol, errors resulting from uncertainties in surface properties and errors resulting from uncertainties in assumptions of aerosol properties. These three errors combine to give overall uncertainties of 1.5 to 2.2 Wm-2 (21-56%) in determining the Southern Hemisphere smoke aerosol forcing at the top of the atmosphere. The range of values depend on which estimate of MODIS retrieval uncertainty is used, either the theoretical calculation (upper bound) or the empirical estimate (lower bound). Strategies that use the satellite data to derive flux directly or use the data in conjunction with ground-based remote sensing and aerosol transport models can reduce these uncertainties.

Remer, Lorraine A.↗
NASA NTRSDate not supplied

Is There A Relationship Between Cornered-Hat Methods and A Residual Approach to Estimate System Uncertainty?

Recently a relationship has been established between a now traditional residual diagnostic used to estimate observation, background and analysis error covariances of interest to data assimilation and the three-cornered hat (3CH) method. An existing extension of the traditional residual diagnostic uses residuals from a fixed lag-1 Kalman smoother to retrieve system (model) error covariance. It is thus natural to ask if an additional relationship can be established between this extended residual method and some form of cornered--hat method. The answer might seem straightforward. Unlike in the standard residual estimation case, attempting to estimate model error amounts to estimating the statistics of a residual quantity itself. As shown in this presentation, in such cases the 3CH method becomes trivial: the sought out uncertainty can be derived directly from the covariance of the residual quantity at hand. In the particular case of estimating model error, one of the corners would have to be composed of vectors providing estimates of model error; these are not typically available in practice. Therefore, at first glance the present work finds no relationship between the lag-1 smoother residual diagnostic for system error estimation and cornered--hat methods. However, the work suggests that a two-tiered 3CH might be all that is necessary for system uncertainty to be obtained with 3CH.

Ricardo Todling↗
NASA NTRS2017

Cosmic Infrared Background Fluctuations and Zodiacal Light

We performed a specific observational test to measure the effect that the zodiacal light can have on measurements of the spatial fluctuations of the near-IR (near-infrared)background. Previous estimates of possible fluctuations caused by zodiacal light have often been extrapolated from observations of the thermal emission at longer wavelengths and low angular resolution or from IRAC (Infrared Array Camera) observations of high-latitude fields where zodiacal light is faint and not strongly varying with time. The new observations analyzed here target the COSMOS (Cosmic Evolution Survey) field at low ecliptic latitude where the zodiacal light intensity varies by factors of approximately 2 over the range of solar elongations at which the field can be observed. We find that the white-noise component of the spatial power spectrum of the background is correlated with the modeled zodiacal light intensity. Roughly half of the measured white noise is correlated with the zodiacal light, but a more detailed interpretation of the white noise is hampered by systematic uncertainties that are evident in the zodiacal light model. At large angular scales (greater than or approximately equal to 100 arcseconds) where excess power above the white noise is observed, we find no correlation of the power with the modeled intensity of the zodiacal light. This test clearly indicates that the large-scale power in the infrared background is not being caused by the zodiacal light.

cosmology: observations aEuro" diffuse radiation a↗
DOE OSTI2025

Weakly supervised anomaly detection for resonant new physics in the dijet final state using proton-proton collisions at $\sqrt{s}$ = 13 TeV with the ATLAS detector

An anomaly detection search for narrow-width resonances beyond the Standard Model that decay into a pair of jets is presented. The search is based on 139 fb −1 of proton-proton collisions at $\sqrt{s}$ = 13 TeV recorded during 2015–2018 with the ATLAS detector at the Large Hadron Collider. The analysis is optimized without a particular signal model and aims to be sensitive to a broad range of new physics. It uses two different machine learning strategies to estimate the background in different signal regions. In each region, a weakly supervised classifier is trained to distinguish this background model from data. The analysis focuses on events with high transverse momentum jets reconstructed as large-radius jets. The mass and substructure of these jets are used as inputs to the classifiers. After a classifier-based selection, the distribution of the invariant mass of the two jets is used to search for potential local excesses. The model-independent results of both the anomaly detection methods show no signs of significant local excesses. In addition to model-independent results, a representative set of signal models is injected into the data, and the sensitivity of the methods to these scenarios is reported.

Aad, G. [Aix-Marseille Université] (ORCID:00000002↗
DOE OSTI2026

Searches for BSM physics using challenging and long-lived signatures with the ATLAS detector

Various theories beyond the Standard Model predict new, long-lived particles with unique signatures which are difficult to reconstruct and for which estimating the background rates is also a challenge. Signatures from displaced and/or delayed decays anywhere from the inner detector to the muon spectrometer, as well as those of new particles with fractional or multiple values of the charge of the electron or high mass stable charged particles are all examples of experimentally demanding signatures. The talk will focus on the most recent results using 13 TeV and 13.6TeV pp collision data collected by the ATLAS detector.

Bruce, Laura [Harvard University]↗
DOE OSTI2026

Applications Of Machine Learning to Gas Plume Analysis In Longwave Infrared Hyperspectral Images

Longwave infrared hyperspectral images can be used for gas plume analysis, as many gases exhibit distinct absorption features in this portion of the electromagnetic spectrum. In practice, accurately identifying weak gas signatures is difficult because the observed radiance is dominated by background radiance, which varies with material, temperature, and viewing conditions. Many gas plume analysis pipelines operate on single images, limiting the ability to leverage spatial and multi-view information that could enhance the analysis. The goal of this dissertation is to explore how machine learning and deep learning methods can complement classical approaches to improve gas plume identification in longwave infrared hyperspectral imagery, and to investigate the use of neural radiance fields for hyperspectral scene reconstruction.

3D Scene Reconstruction↗
NASA NTRS1991

X-ray emission from starburst galaxies

The results are reported of an investigation of X-ray emission from a sample of 53 IRAS-selected candidate starburst galaxies. Superposed soft and hard X-ray emission from these galaxies in the Einstein-IPC and HEAO-1 A-2 and A-4 energy bands, which span 0.5 to 160 keV, is detected at the 99.6 percent confidence level, after allowing for confusion noise in the HEAO-1 data. Above 15 keV the confidence level is 97 percent. The combined spectrum is flat, with a (photon) power-law index of 1.0 +/- 0.3. The contribution of the population of sources represented by this sample to the 3-50 keV residual cosmic X-ray background is estimated to be at least about 4 percent assuming no evolution. Moderate evolution, for which there is some observational evidence, increases this fractional contribution to about 26 percent.

Rephaeli, Yoel↗
NASA NTRS1999

X-Ray Continua of Broad Absorption Line Quasars

The targets for this program, PG1416-129 and LBQS 2212-1759 were known to be Broad Absorption Line Quasars (BALQSOs). BALQSOs are highly absorbed in soft X-rays. Good high energy response of Rossi-XTE made them ideal targets for observation. We observed LBQS 2212-1759 with PCA. We have now analyzed the data and found that the source was not detected. Since our target was expected to be faint, reliable estimate of background was very important. With the release of new FTOOLS (version 4.1) we were able to do so. We also analyzed a well known bright object and verified our results with the published data. This gave us confidence in the non-detection of our target LBQS 2212-1759. We are currently investigating the implications of this non-detection. Due to some scheduling problems, our second target PG1416-129 was not observed in A01. It was observed on 06/26/98. This target was detected with RXTE. We are now working on the spectral analysis with XSPEC.

Mathur, S.↗
NASA NTRS1995

Object Detection in Natural Backgrounds Predicted by Discrimination Performance and Models

In object detection, an observer looks for an object class member in a set of backgrounds. In discrimination, an observer tries to distinguish two images. Discrimination models predict the probability that an observer detects a difference between two images. We compare object detection and image discrimination with the same stimuli by: (1) making stimulus pairs of the same background with and without the target object and (2) either giving many consecutive trials with the same background (discrimination) or intermixing the stimuli (object detection). Six images of a vehicle in a natural setting were altered to remove the vehicle and mixed with the original image in various proportions. Detection observers rated the images for vehicle presence. Discrimination observers rated the images for any difference from the background image. Estimated detectabilities of the vehicles were found by maximizing the likelihood of a Thurstone category scaling model. The pattern of estimated detectabilities is similar for discrimination and object detection, and is accurately predicted by a Cortex Transform discrimination model. Predictions of a Contrast- Sensitivity- Function filter model and a Root-Mean-Square difference metric based on the digital image values are less accurate. The discrimination detectabilities averaged about twice those of object detection.

Ahumada, A. J., Jr.↗