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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗
NASA NTRS2013

Cross-correlating Cosmic IR and X-ray Background Fluctuations: Evidence of Significant Black Hole Populations Among the CIB Sources

In order to understand the nature of the sources producing the recently uncovered cosmic infrared background (CIB) fluctuations, we study cross-correlations between the fluctuations in the source-subtracted CIB from Spitzer/IRAC data and the unresolved cosmic X-ray background from deep Chandra observations. Our study uses data from the EGS/AEGIS field, where both data sets cover an approx = 8' x 45' region of the sky. Our measurement is the cross-power spectrum between the IR and X-ray data. The cross-power signal between the IRAC maps at 3.6 micron and 4.5 micron and the Chandra [0.5-2] keV data has been detected, at angular scales approx >20'', with an overall significance of approx = 3.8 sigma and approx. = 5.6 sigma, respectively. At the same time we find no evidence of significant cross-correlations at the harder Chandra bands. The cross-correlation signal is produced by individual IR sources with 3.6 micron and 4.5 micron magnitudes m(sub AB) approx. > 25-26 and [0.5-2] keV X-ray fluxes << 7 × 10(exp -177 erg sq. cm/ s. We determine that at least 15%-25% of the large scale power of the CIB fluctuations is correlated with the spatial power spectrum of the X-ray fluctuations. If this correlation is attributed to emission from accretion processes at both IR and X-ray wavelengths, this implies a much higher fraction of accreting black holes than among the known populations. We discuss the various possible origins for the cross-power signal and show that neither local foregrounds nor the known remaining normal galaxies and active galactic nuclei can reproduce the measurements. These observational results are an important new constraint on theoretical modeling of the near-IR CIB fluctuations. local foregrounds, nor the known remaining normal galaxies and active galactic nuclei (AGN) can reproduce the measurements. These observational results are an important new constraint on theoretical modeling of the near-IR CIB fluctuations

Cosmic X-ray Background (CXB)↗
DOE OSTI2024

Graph neural networks for CO 2 solubility predictions in Deep Eutectic Solvents

Deep Eutectic Solvents (DESs) are a promising class of solvents for CO 2 capture. DESs are complex mixtures that can be designed to optimize CO solubility and overall capture process efficiency. However, the vast design landscape of DES mixtures makes experimental investigation prohibitive; as such, there is a need for computational models that can quickly and efficiently navigate the design space and inform data collection efforts. In this work, we propose Graph Neural Network (GNN) models for predicting CO 2 solubility for DESs; the GNN leverages a mixture graph representation that captures the molecular structure of the DES components as well as their intermolecular interactions. Here, we compare the GNN framework against alternative architectures (neural networks, graph convolution networks, and random forests) and data representations (molecular fingerprints, sigma profiles, and graphs). We show that the proposed approach offers superior predictive performance; specifically, we show that solubility can be predicted reliably directly from molecular structure (without the need of using sigma profiles as proposed in previous studies). This result is important, as obtaining sigma profiles requires expensive density functional theory computations. We also explored the ability of GNNs to predict solubility for new DES mixtures and operating conditions. We found that the model extrapolates across temperature reliably. However, we also found deficiencies in the ability of the model to predict solubility for DES mixtures, pressures, and molar ratio not included in the training sets; we show that this is due to an inherent lack of chemical diversity in datasets available in the literature. The proposed computational capabilities can thus help navigate the design space of DES and inform data collection efforts. Our models, data, and benchmarks are shared as Python code implemented in Jupyter notebooks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

Determining highly elliptical earth orbits with VLBI and Delta-VLBI

This paper shows the feasibility of using Very Long Baseline Interferometric (VLBI) data acquired by the Deep Space Network to navigate highly elliptical earth orbiting satellites. The planned mission orbit of the Ion Release Module of the Active Magnetospheric Particle Tracer Explorers is used as a reference for developing strategies and provides the first opportunity for a possible flight demonstration with a spacecraft in a highly elliptical orbit. The navigation accuracy improvements achievable with VLBI and Delta-VLBI data types are determined for comparison with the capabilities of a reference Doppler strategy. The results show that strategies using wideband Delta-VLBI measurements taken near periapse perform best, determining apoapse position to an order of magnitude better than conventional Doppler, while also substantially reducing the required antenna support.

Frauenholz, R. B.↗
DOE OSTI2018

Deep Learning for Unsupervised Insider Threat Detection in Structured Cyber Security Data Streams

Analysis of an organization's computer network activity is a key component of early detection and mitigation of insider threat, a growing concern for many organizations. Raw system logs are a prototypical example of streaming data that can quickly scale beyond the cognitive power of a human analyst. As a prospective filter for the human analyst, we present an online unsupervised deep learning approach to detect anomalous network activity from system logs in real time. Our models decompose anomaly scores into the contributions of individual user behavior features for increased interpretability to aid analysts reviewing potential cases of insider threat. Using the CERT Insider Threat Dataset v6.2 and threat detection recall as our performance metric, our novel deep and recurrent neural network models outperform Principal Component Analysis, Support Vector Machine and Isolation Forest based anomaly detection baselines. For our best model, the events labeled as insider threat activity in our dataset had an average anomaly score in the 95.53 percentile, demonstrating our approach's potential to greatly reduce analyst workloads.

Deep Learning, Insider Threat↗
NASA NTRS1984

Quantitative satellite applications - Tropical cyclone intensity monitoring and track forecasting

An assessment is made of the effectiveness of VISSR Atmospheric Sounder (VAS) data gathered over the North Atlantic Ocean area in the 1982 and 1983 hurricane seasons. By the end of the 1983 season, progress had been made in providing high quality analyses and displays of mass, motion and moisture patterns for evaluation. A comparison of mean forecast errors for several different operational models, official forecasts, and the VAS Trajectory model for selected cases in which VAS deep layer mean wind data were available show VAS accuracies that are comparable with those of the official forecasts.

Velden, C. S.↗
DOE OSTI2019

Deep multiscale model learning

The objective of this article is to design novel multi-layer neural networks for multiscale simulations of flows taking into account the observed fine data and physical modeling concepts. Our approaches use deep learning techniques combined with local multiscale model reduction methodologies to predict flow dynamics. Using reduced-order model concepts is important for constructing robust deep learning architectures since the reduced-order models provide fewer degrees of freedom. We consider flow dynamics in porous media as multi-layer networks in this work. More precisely, the solution (e.g., pressures and saturation) at the time instant n+1 depends on the solution at the time instant n and input parameters, such as permeability fields, forcing terms, and initial conditions. One can regard the solution as a multi-layer network, where each layer, in general, is a nonlinear forward map and the number of layers relates to the internal time steps. We will rely on rigorous model reduction concepts to define unknowns and connections between layers. It is critical to use reduced-order models for this purpose, which will identify the regions of influence and the appropriate number of variables. Furthermore, due to the lack of available observed fine data, the reduced-order model can provide us sufficient inexpensive data as needed. The designed deep neural network will be trained using both coarse simulation data which is obtained from the reduced-order model and observed fine data. We will present the main ingredients of our approach and numerical examples. Numerical results show that using deep learning with data generated from multiscale models as well as available observed fine data, we can obtain an improved forward map which can better approximate the fine scale model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

( Chandra Chandra) counterparts of CANDELS GOODS-S sources

Improving the capabilities of detecting faint X-ray sources is fundamental for increasing the statistics on faint high-z active galactic nuclei (AGNs) and star-forming galaxies (SFGs). We performed a simultaneous maximum likelihood point-spread function fit in the [0.5–2] keV and [2–7] keV energy bands of the 4 Ms Chandra Deep Field South (CDFS) data at the position of the 34,930 CANDELS H-band selected galaxies. For each detected source we provide X-ray photometry and optical counterpart validation. We validated this technique by means of a ray-tracing simulation. We detected a total of 698 X-ray point sources with a likelihood L>4.98 (i.e., >2.7σ). We show that prior knowledge of a deep sample of optical–NIR galaxies leads to a significant increase in the detection of faint (i.e., ∼10{sup −17} cgs in the [0.5–2] keV band) sources with respect to “blind” X-ray detections. By including previous X-ray catalogs, this work increases the total number of X-ray sources detected in the 4 Ms CDFS, CANDELS area to 793, which represents the largest sample of extremely faint X-ray sources assembled to date. Our results suggest that a large fraction of the optical counterparts of our X-ray sources determined by likelihood ratio actually coincides with the priors used for the source detection. Most of the new detected sources are likely SFGs or faint, absorbed AGNs. We identified a few sources with putative photometric redshift z > 4. Despite the low number statistics and the uncertainties on the photo z, this sample significantly increases the number of X-ray-selected candidate high-z AGNs.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1979

Development and analysis of a twelfth degree and order gravity model for Mars

Satellite geodesy techniques previously applied to artificial earth satellites have been extended to obtain a high-resolution gravity field for Mars. Two-way Doppler data collected by 10 Deep Space Network (DSN) stations during Mariner 9 and Viking 1 and 2 missions have been processed to obtain a twelfth degree and order spherical harmonic model for the martian gravitational potential. The quality of this model was evaluated by examining the rms residuals within the fit and the ability of the model to predict the spacecraft state beyond the fit. Both indicators show that more data and higher degree and order harmonics will be required to further refine our knowledge of the martian gravity field. The model presented shows much promise, since it resolves local gravity features which correlate highly with the martian topography. An isostatic analysis based on this model, as well as an error analysis, shows rather complete compensation on a global (long wavelength) scale. Though further model refinements are necessary to be certain, local (short wavelength) features such as the shield volcanos in Tharsis appear to be uncompensated. These are interpreted to place some bounds on the internal structure of Mars.

Christensen, E. J.↗
DOE OSTI2020

AI for Science: Report on the Department of Energy (DOE) Town Halls on Artificial Intelligence (AI) for Science

The report documents the DOE Town Halls held during 2019 at Argonne National Laboratory, Oak Ridge National Laboratory, Lawrence Berkeley National Laboratory, and in Washington, DC. From July to October 2019, the Argonne, Oak Ridge, and Berkeley National Laboratories hosted a series of four town hall meetings attended by more than 1,000 U.S. scientists and engineers. The goal of the town hall series was to examine scientific opportunities in the areas of artificial intelligence (AI), Big Data, and high-performance computing (HPC) in the next decade, and to capture the big ideas, grand challenges, and next steps to realizing these opportunities. In this report and in the Department of Energy (DOE) laboratory community, we use the term “AI for Science” to broadly represent the next generation of methods and scientific opportunities in computing, including the development and application of AI methods (e.g., machine learning, deep learning, statistical methods, data analytics, automated control, and related areas) to build models from data and to use these models alone or in conjunction with simulation and scalable computing to advance scientific research. The AI for Science town hall discussions focused on capturing the transformational uses of AI that employ HPC and/or data analysis, leveraging data sets from HPC simulations or instruments and user facilities, and addressing scientific challenges unique to DOE user facilities and the agency’s wide-ranging fundamental and applied science enterprise.

36 MATERIALS SCIENCE↗
NASA NTRS2020

Results of the Deep Space Atomic Clock Deep Space Navigation Analog Experiment

The timing and frequency stability provided by the Deep Space Atomic Clock (DSAC) is almost comparable with the Deep Space Network’s ground clocks, and will enable one-way radiometric measurements with accuracy equivalent to cur- rent two-way tracking data. A demonstration unit of the clock was launched into low Earth orbit on June 25, 2019, for the purpose of validating DSAC’s perfor- mance in the space environment. GPS data collected throughout the mission was utilized not only for precise clock estimation, but also as a proxy for deep space tracking data to conduct the Deep Space Navigation Analog Experiment. Through careful processing of GPS Doppler data and limited modeling fidelity representa- tive of deep space navigation capabilities, the analog orbit solutions are compared to higher-fidelity solutions, demonstrating DSAC’s viability as a navigation instru- ment in conditions typical for a low altitude Mars orbiter.

Stuart, Jeffrey↗
DOE OSTI2020

Smart Scattering Scanning Near-Field Optical Microscopy

Scattering scanning near-field optical microscopy (s-SNOM) provides spectroscopic imaging from molecular to quantum materials with few nanometer deep subdiffraction limited spatial resolution. However, in its conventional implementation s-SNOM is slow to effectively acquire a series of spatio-spectral images, especially with large fields of view. This problem is further exacerbated for weak resonance contrast or when using light sources with limited spectral irradiance. Indeed, the generally limited signal-to-noise ratio prevents sampling a weak signal at the Nyquist sampling rate. Here, we demonstrate how acquisition time and sampling rate can be significantly reduced by using compressed sampling, matrix completion, and adaptive random sampling, while maintaining or even enhancing the physical or chemical image content. We use fully sampled real data sets of molecular, biological, and quantum materials as ground-truth physical data and show how deep under-sampling with a corresponding reduction of acquisition time by 1 order of magnitude or more retains the core s-SNOM image information. We demonstrate that a sampling rate of up to 6× smaller than the Nyquist criterion can be applied, which would provide a 30-fold reduction in the data required under typical experimental conditions. Furthermore, our smart s-SNOM approach is generally applicable and provides systematic full spatio-spectral s-SNOM imaging with a large field of view at high spectral resolution and reduced acquisition time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Deep Learning for Full Waveform Inversion of Elastic Active-Source Seismic Data to Estimate P-Wave Velocity Models

Seismic imaging methods are critical for Global Security and Energy & Homeland Security missions and activities that rely on subsurface characterization, but traditional methods remain computationally expensive and require significant labor hours and expertise to execute. Within the past few years, machine learning (ML), namely deep learning (DL), has been used to develop data-driven end-to-end full waveform inversion (FWI) methods to estimate 2D P-wave velocity (Vp) models in a fraction of the time as conventional FWI. These methods, however, are trained on simplistic acoustic wave seismic data and Vp models that are not realistic nor representative of real-world observations, leaving a large gap between the state-of-the-art and deployable, feasible, and practical DL FWI methods. Here, we generate a synthetic active-source, 3D, elastic wave seismic data set and a variety of Vp models with realistic geologic structure for training DL FWI methods. We evaluate six different methods that have performed well for acoustic DL FWI or medical imaging tasks using our more realistic dataset. We find that these six trained models do not match the performance of published acoustic end-to-end DL FWI methods, indicating more training data may be needed, physics may need to be incorporated to achieve good accuracy at the sacrifice of the end-to-end advantage, and/or novel methods need to be developed to enable end-to-end DL FWI methods to perform well for real-world seismic data.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
NASA NTRS1986

Comparison of two models of total electron content at three midlatitude stations

TEC values calculated with the Bent model were used instead of actual data for the Madrid Deep Space Communications Complex during most of the Voyager Uranus approach and encounter. These values were used from December 1, 1985 to January 16, 1986 for Voyager Uranus, and for March 1986 to support the Pathfinder mission; thus, the Halley flybys of Vega 1 and 2 and Giotto were included. Provided sunspot numbers are below 130, the Bent model can be used with confidence to calculate TEC values for midlatitude stations when actual TEC data or monthly averages are not available.

Royden, H. N.↗
NASA NTRS1977

Radio-frequency interference effects of continuous sinewave signals on telemetry data

Continuous sinewave interference effects on telemetry data obtained at the Goldstone Deep Space Station (DSS 11) were analyzed. The continuous sinewave interference is treated as an extraneous noise. Empirical telemetry data degradation and drop-lock models were then developed based on test data and certain physical characteristics of the telemetry data processing system. These models will be used as a portion of the radio frequency interference detection tools in the first version of the Deep Space Interference Prediction software.

Low, P. W.↗
NASA NTRS2011

Validating and Improving Long-Term Aerosol Data Records from SeaWiFS

Natural and anthropogenic aerosols influence the radiative balance of the Earth through direct and indirect interactions with incoming solar radiation. However, the quantification of these interactions and their ultimate effect on the Earth's climate still have large uncertainties. This is partly due to the limitations of current satellite data records which include short satellite lifetimes, retrieval algorithm uncertainty, or insufficient calibration accuracy. We have taken the first steps in overcoming this hurdle with the production and public release of an aerosol data record using the radiances from the Sea-viewing Wide Field-of-View Sensor (Sea WiFS). Sea WiFS was launched in late 1997 and provided exceptionally well-calibrated top-of-atmosphere radiance data until December 2010, more than 13 years. We have partnered this data with an expanded Deep Blue aerosol retrieval algorithm. In accordance with Deep Blue's original focus, the latest algorithm retrieves aerosol properties not only over bright desert surfaces, but also over oceans and vegetated surfaces. With this combination of a long time series and global algorithm, we can finally identify the changing patterns of regional aerosol loading and provide insight into longterm variability and trends of aerosols on regional and global scales. In this work, we provide an introduction to Sea WiFS, the current algorithms, and our aerosol data records. We have validated the data over land and ocean with ground measurements from the Aerosol Robotic Network (AERONET) and compared them with other satellites such as MODIS and MISR. Looking ahead to the next data release, we will also provide details on the implemented and planned algorithm improvements, and subsequent validation results.

Bettenhausen, Corey↗
DOE OSTI2025

Intercomparison of Deep Learning Model Architectures for Atmospheric River Prediction

With a rapid surge in the application of machine learning (ML) for a diverse range of tasks in climate science, the present study addresses a challenge for climate scientists when selecting the optimal ML or deep learning (DL) architecture for a given application. In particular, a DL intercomparison study was performed with a focus on forecasting the position of atmospheric rivers (ARs) on short-range time scales (up to 5-day lead times). AR predictions from multiple DL architectures, including various types of convolutional autoencoders and a vision transformer (ViT), were compared against ECMWF ERA5 reanalysis and hindcasts from a global climate model. DL models with similar trainable parameters were trained on ERA5 reanalysis data and AR positions derived from a thresholding algorithm to ensure a fair comparison among the DL models. Each model’s performance and accuracy in forecasting AR location and key input fields within a 5-day window were assessed using metrics of root-mean-square error, anomaly correlation, and mean intersection over union. The ViT architecture outperformed other autoencoder models in most of the metrics. Incorporating additional meteorological fields only yielded slight improvements in forecasting certain fields at longer lead times. The results also suggest that a smaller number of input time steps or smaller number of autoregressive steps can achieve better prediction skills, while also improving the overall computational efficiency. This research offers valuable insights into the strengths and weaknesses of different DL techniques for AR forecasting, hopefully guiding the development of improved models for forecasting this phenomenon.

54 ENVIRONMENTAL SCIENCES↗