Search NASA⌕ Search

SEARCH · Search NASA

Results for “synthetic data sets”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Simultaneous Development and Robust Optimization of a Microstructure Dependent Material

Recent microstructure characterization techniques combined with Symbolic Regression(SR)analysis has been proven to generate white box plasticity models well suited for incorporation into FEA software.The current work builds upon those efforts and demonstrates the applicability of Sequential Monte-Carlo (SMC) methods within SR analysis to condense model development and robust optimization into a single, co-dependent process. In this project, SMC methods provide a mechanism through which the observed microstructure features and associated variability can be incorporated into the discovery phase of model development and simultaneously recover approximate parameter distributions through SR analysis. The demonstration utilized a data set consisting of tensile test results from a limited number of sample specimens with corresponding EBSD data from which microstructure features were characterized.The maximum threshold stress model in the Visco-Plastic Self-Consistent (VPSC) code developed by Los Alamos National Laboratories was calibrated using mechanical test data.Synthetic volume elements with statistically equivalent microstructure were generated with DREAM3Dbased on the observed EBSD data. VPSC was used to simulate the corresponding tensile test response for each of the synthetic volume elements. The simulated microstructure and tensile test data was used astraining datafor SMC-SR algorithm and the resulting model was validated with data from the original empirical data set.

Karl Garbrecht↗
DOE OSTI2022

Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. Here, in this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have independent parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have correlated noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac

The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally corrected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift correction, employing a prototype-based classification approach. Our model utilizes a 34-dimensional feature set (RF settings and BPM readings) and leverages a 7x27 response matrix for system modeling. To overcome limited real-world data, we generate synthetic data, enhancing model training and generalizability. Custom loss functions, including a surrogate energy-consistent loss and a temporal smoothness constraint, ensure physically plausible drift predictions. The goal is a robust system for autonomous phase adjustments, ensuring stable beam acceleration and reduced manual intervention.

Chichili, R. R. [Illinois U., Chicago]↗
DOE OSTI2025

Automated RF Phase Adjustment for Beam Stabilization in the Fermilab Linac

The Fermilab Linac experiences longitudinal beam phase drift, leading to increased particle loss, conventionally cor- rected through labor-intensive manual RF adjustments. This project explores machine learning-based automation for drift correction, employing a prototype-based classification approach. Our model utilizes a 34-dimensional feature set (RF settings and BPM readings) and leverages a 7x27 response matrix for system modeling. To overcome limited real-world data, we generate synthetic data, enhancing model training and generalizability. Custom loss functions, including a sur- rogate energy-consistent loss and a temporal smoothness constraint, ensure physically plausible drift predictions. The goal is a robust system for autonomous phase adjustments, ensuring stable beam acceleration and reduced manual intervention.

Chichili, R. R. [U. Illinois, Chicago]↗
NASA NTRS1985

Simulations of SAR wave spectra using high spectral resolution estimates from the SCR and ROWS instruments

A numerical model for predicting the synthetic aperture radar (SAR) image of a moving ocean surface is described, and results are presented for two SIR-B data sets collected off the coast of Chile. Wave height spectra measured by the NASA radar ocean wave spectrometer (ROWS) and surface contour radar (SCR) were used as inputs to this model, and results are compared with actual SIR-B image spectra from orbits 91 and 106.

Lyzenga, D.↗
NASA NTRS1986

Numerical simulation of synthetic aperture radar image spectra for ocean waves

A numerical model for predicting the synthetic aperture radar (SAR) image of a moving ocean surface is described, and results are presented for two SIR-B data sets collected off the coast of Chile. Wave height spectra measured by the NASA radar ocean wave spectrometer (ROWS) were used as inputs to this model, and results are compared with actual SIR-B image spectra from orbits 91 and 106. Additional parametric variations are presented to illustrate the effects of nonlinearities in the imaging process.

Lyzenga, D. R.↗
DOE OSTI2020

Isotopic analysis of plutonium measured with very thick absorbers and high-granularity spectra

FRAM is LANL-designed software designed to extract plutonium and uranium isotopic information from gamma ray spectra. This is an important capability for NEST and STAB; however, FRAM performance has been poor when applied to field data collected in these programs. FRAM Version 5 (commercially available) was fine-tuned and validated for laboratory quality measurements but not for measurements of the high-mass and heavily shielded SNM items encountered in NEST. FRAM is untested for spectra collected with high-granularity lower resolution instruments such as the new 8-MeV Detective-X favored by STAB or the new custom electrocooled systems coming into use for NEST. FRAM Version 6.1 (unreleased) has improvements in physics capabilities, and parameter sets may be adapted that are more specific to NEST and STAB, which is what we have attempted for this assignment. Thick shielding attenuates gamma rays profoundly, especially those at low energy, making them too weak to be measured. The missing low-energy peaks force the analysis to employ only the highest-energy peaks. We adapted FRAM 6.1 for analysis of plutonium spectra obtained with very thick Pb filters (a surrogate for NEST-type configurations). New parameter sets employing only very high energy peaks were created for the analysis. High granularity and low resolution make deconvolution of the 600-keV region particularly challenging, and we have worked to optimally extract information from this region. The results shown in this paper give high confidence that we are on the right track. We used a combination of real and synthetically-modified data. Remaining work includes testing the new software and parameters against genuine data collected with Detective-X and new NEST systems, but such datasets at the moment are unavailable. It also remains to develop and test parameter sets specific to uranium and the new detectors.

07 ISOTOPE AND RADIATION SOURCES↗
NASA NTRS1984

Analysis of upper mantle structure using wave field continuation of P waves

Wave field continuation theory, which allows transformation of the seismic record section data directly into velocity-depth space, is tested for upper mantle analysis using a large array-recorded data set obtained at the 200-station Caltech-USGS Southern California Seismic Network that is representative of the structure beneath the gulf of California. The method's resolution capability is illustrated by the comparison of the slant stacks and downward continuation of both synthetic and data record sections. It is stressed that when high-quality, densely sampled digital data are available, the technique is easy to implement, provides an inversion which contains all the data in the global format, and produces an objective estimate of depth resolution as a function of ray parameter.

Walck, M. C.↗
NASA NTRS1993

Techniques and applications of SAR interferometry for ERS-1

The ERS-1 Synthetic Aperture Radar (SAR) demonstrated that it is possible to measure high resolution topography over large areas using interferometry with repeat orbit data sets. Given that the ERS-1 orbit is known with high precision, an automatic system for generation of interferograms is presented. Least squares estimation using ground control points provides an accurate method for determining the precise interferometric baseline. The system parameters which affect the accuracy of the Digital Elevation Models (DEM's) are errors in the interferometric baseline, decorrelation caused by baseline separation, thermal noise, and surface change. An adaptive filter based on the local interferometric coherence was developed to improve phase unwrapping of the interferogram. Final accuracies of the DEM's generated for single scene pairs in Alaska (U.S.) and Bonn (Germany) are on the order of 5 to 10 m.

Werner, Charles L.↗
NASA NTRS1995

User guide to the Magellan synthetic aperture radar images

The Magellan radar-mapping mission collected a large amount of science and engineering data. Now available to the general scientific community, this data set can be overwhelming to someone who is unfamiliar with the mission. This user guide outlines the mission operations and data set so that someone working with the data can understand the mapping and data-processing techniques used in the mission. Radar-mapping parameters as well as data acquisition issues are discussed. In addition, this user guide provides information on how the data set is organized and where specific elements of the set can be located.

Wall, Stephen D.↗
DOE OSTI2020

LyaCoLoRe : synthetic datasets for current and future Lyman-α forest BAO surveys

The statistical power of Lyman-α forest Baryon Acoustic Oscillation (BAO) measurements is set to increase significantly in the coming years as new instruments such as the Dark Energy Spectroscopic Instrument deliver progressively more constraining data. Generating mock datasets for such measurements will be important for validating analysis pipelines and evaluating the effects of systematics. With such studies in mind, here we present LyaCoLoRe: a package for producing synthetic Lyman-α forest survey datasets for BAO analyses. LyaCoLoRe transforms initial Gaussian random field skewers into skewers of transmitted flux fraction via a number of fast approximations. In this work we explain the methods of producing mock datasets used in LyaCoLoRe, and then measure correlation functions on a suite of realisations of such data. We demonstrate that we are able to recover the correct BAO signal, as well as large-scale bias parameters similar to literature values. Finally, we briefly describe methods to add further astrophysical effects to our skewers — high column density systems and metal absorbers — which act as potential complications for BAO analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Thomson scattering (TS) diagnostics provide reliable, minimally perturbative measurements of fundamental plasma parameters, such as electron density (⁠n e ) and electron temperature (⁠T e ⁠). Deep neural networks can provide accurate estimates of ⁠n e and T e when conventional fitting algorithms may fail, such as when TS spectra are dominated by noise, or when fast analysis is required for real-time operation. Although deep neural networks typically require large training sets, transfer learning can improve model performance on a target task with limited data by leveraging pre-trained models from related source tasks, where select hidden layers are further trained using target data. We present five architecturally diverse deep neural networks, pre-trained on synthetic TS data and adapted for experimentally measured TS data, to evaluate the efficacy of transfer learning in estimating n e and T e in both the collective and non-collective scattering regimes. We evaluate errors in n e and T e estimates as a function of training set size for models trained with and without transfer learning, and we observe decreases in model error from transfer learning when the training set contains ≲ 200 experimentally measured spectra.

Artificial neural networks↗
NASA NTRS2019

Target Optimal Aperture Selection

This document describes the computation of optimal pixels for planetary transit targets. The method we describe is based on that used in the End-to-End Model (ETEM), which simulates Kepler science output. An optimal aperture for a target is defined as the set of pixels which maximizes the signal-to-noise ratio (SNR) for that target. The optimal aperture for a target is determined by using catalog data to generate, for that target, a synthetic image with all background stars and signals and a second synthetic image with the target star's flux only. These images are incorporated into a noise model, from which the SNR of each pixel is computed. The pixels around a target are summed in an order that maximizes the SNR with each term. As dimmer and dimmer pixels contribute to this sum, the SNR reaches a maximum and further terms decrease the SNR. The set of pixels whose SNR sums to this maximum value defines the optimal aperture.

Bryson, Stephen↗
NASA NTRS1980

Seasat: - JASIN workshop report. Volume 1: Findings and conclusions

Data acquired from the Joint Air-Sea Interaction (JASIN) Experiment and Seasat data from overpasses that covered the JASIN area were compared and evaluated. The JASIN data were used as a high quality withheld data set. The Seasat data were prepared for each instrument: the Seasat-A Satellite Scatterometer (SASS), the Scanning Multichannel microwave radiometer (SMMR), and the synthetic aperture radar (SAR). The following results demonstrate the performance of Seasat: it was possible to identify mesoscale systems with the SASS as well as fronts; the SMMR provided accurate water vapor content of the atmosphere; the SAR showed evidence of helical rolls in the marine boundary layer; a SASS anomaly could be traced back to thunderstorm activity.

Source record↗
NASA NTRS1984

Synthetic aperture radar images of ocean waves, theories of imaging physics and experimental tests

The physical mechanism for the synthetic Aperture Radar (SAR) imaging of ocean waves is investigated through the use of analytical models. The models are tested by comparison with data sets from the SEASAT mission and airborne SAR's. Dominant ocean wavelengths from SAR estimates are biased towards longer wavelengths. The quasispecular scattering mechanism agrees with experimental data. The Doppler shift for ship wakes is that of the mean sea surface.

Vesecky, J. F.↗
NASA NTRS1984

Radiation effects response data for synthetic organic insulation and dielectrics

Existing radiation response data for 130 materials of the synthetic organic insulation and dielectric class are analyzed, and thresholds and 25-percent change dose levels for these materials are presented. Both the lowest reported threshold dose (LTD) and the 25-percent change dose level are found to vary widely among the different insulators (used as spacecraft components). The LTD is tabulated to indicate the levels where radiation effects become apparent. These level are of use in setting a lower limit where changes can be expected, and could be used to exempt synthetic insulation and dielectrics from radiation considerations when the environments are substantially below this limit. Cautions to be observed in applying the data to current problems are presented.

Bouquet, F. L.↗
NASA NTRS1992

Mid-infrared spectra of Martian komantiite

Synthetic mid-infrared emissive spectra of basalts of possible Martian komatiitic compositions were generated to show what might be seen in data returned from the Thermal Emission Spectrometer (TES). TES is flying on the Mars Observer arriving at Mars in late 1993. The synthetic Martian komatiite spectra were generated using mineralogy based on Shergottite, Nakhlite, and Chassigny (SNC) meteorites and Viking X-Ray Fluorescence (XRF) data. These data sets provide strong evidence for the existence of ultramafic lavas on Mars, particularly lavas of komatiitic composition.

Reyes, David P.↗
DOE OSTI2021

Detection Limits of Low-mass, Long-period Exoplanets Using Gaussian Processes Applied to HARPS-N Solar Radial Velocities

Radial velocity (RV) searches for Earth-mass exoplanets in the habitable zone around Sun-like stars are limited by the effects of stellar variability on the host star. In particular, suppression of convective blueshift and brightness inhomogeneities due to photospheric faculae/plage and starspots are the dominant contribution to the variability of such stellar RVs. Gaussian process (GP) regression is a powerful tool for statistically modeling these quasi-periodic variations. We investigate the limits of this technique using 800 days of RVs from the solar telescope on the High Accuracy Radial velocity Planet Searcher for the Northern hemisphere (HARPS-N) spectrograph. These data provide a well-sampled time series of stellar RV variations. Into this data set, we inject Keplerian signals with periods between 100 and 500 days and amplitudes between 0.6 and 2.4 m s{sup −1}. We use GP regression to fit the resulting RVs and determine the statistical significance of recovered periods and amplitudes. We then generate synthetic RVs with the same covariance properties as the solar data to determine a lower bound on the observational baseline necessary to detect low-mass planets in Venus-like orbits around a Sun-like star. Our simulations show that discovering planets with a larger mass (∼0.5 m s{sup −1}) using current-generation spectrographs and GP regression will require more than 12 yr of densely sampled RV observations. Furthermore, even with a perfect model of stellar variability, discovering a true exo-Venus (∼0.1 m s{sup −1}) with current instruments would take over 15 yr. Therefore, next-generation spectrographs and better models of stellar variability are required for detection of such planets.

47 OTHER INSTRUMENTATION↗