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All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Bright Opportunities for Atmospheric Characterization of Small Planets: Masses and Radii of K2-3 b, c, and d and GJ3470 b from Radial Velocity Measurements and Spitzer Transits

We report improved masses, radii, and densities for four planets in two bright M-dwarf systems, K2-3 and GJ3470, derived from a combination of new radial velocity and transit observations. Supplementing K2 photometry with follow-up Spitzer transit observations refined the transit ephemerides of K2-3 b, c, and d by over a factor of 10. We analyze ground-based photometry from the Evryscope and Fairborn Observatory to determine the characteristic stellar activity timescales for our Gaussian Process fit, including the stellar rotation period and activity region decay timescale. The stellar rotation signals for both stars are evident in the radial velocity data and is included in our fit using a Gaussian process trained on the photometry. We find the masses of K2-3 b, K2-3 c, and GJ3470 b to be 6.48(+0.99,-0.93), 2.14(+1.08,-1.28), and 12.58(+1.31,-1.28)Mꚛ, respectively. K2-3 d was not significantly detected and has a 3σ upper limit of 2.80 Mꚛ. These two systems are training cases for future TESS systems; due to the low planet densities (ρ < 3.7 g/cu.cm) and bright host stars (K < 9 mag), they are among the best candidates for transmission spectroscopy in order to characterize the atmospheric compositions of small planets.

Molly R. Kosiarek↗
NASA NTRS2013

Adaptive Sensing of Time Series with Application to Remote Exploration

We address the problem of adaptive informationoptimal data collection in time series. Here a remote sensor or explorer agent throttles its sampling rate in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility -- all collected datapoints lie in the past, but its resource allocation decisions require predicting far into the future. Our solution is to continually fit a Gaussian process model to the latest data and optimize the sampling plan on line to maximize information gain. We compare the performance characteristics of stationary and nonstationary Gaussian process models. We also describe an application based on geologic analysis during planetary rover exploration. Here adaptive sampling can improve coverage of localized anomalies and potentially benefit mission science yield of long autonomous traverses.

artificial intelligence↗
NASA NTRS1994

Quantitative estimation of granitoid composition from thermal infrared multispectral scanner (TIMS) data, Desolation Wilderness, northern Sierra Nevada, California

We have produced images that quantitatively depict modal and chemical parameters of granitoids using an image processing algorithm called MINMAP that fits Gaussian curves to normalized emittance spectra recovered from thermal infrared multispectral scanner (TIMS) radiance data. We applied the algorithm to TIMS data from the Desolation Wilderness, an extensively glaciated area near the northern end of the Sierra Nevada batholith that is underlain by Jurassic and Cretaceous plutons that range from diorite and anorthosite to leucogranite. The wavelength corresponding to the calculated emittance minimum lambda(sub min) varies linearly with quartz content, SiO2, and other modal and chemical parameters. Thematic maps of quartz and silica content derived from lambda(sub min) values distinguish bodies of diorite from surrounding granite, identify outcrops of anorthosite, and separate felsic, intermediate, and mafic rocks.

Sabine, Charles↗
NASA NTRS2019

OGLE-2017-BLG-1186: First Application of Asteroseismology and Gaussian Processes to microlensing

We present the analysis of the event OGLE-2017-BLG-1186 from the 2017 Spitzer microlensing campaign. This is a remarkable microlensing event because its source is photometrically bright and variable, which makes it possible to perform an asteroseismic analysis using ground-based data. We find that the source star is an oscillating red giant with average timescale of ∼9 d. The asteroseismic analysis also provides us source properties including the source angular size (∼27 μas) and distance (∼11.5 kpc), which are essential for inferring the properties of the lens. When fitting the light curve, we test the feasibility of Gaussian processes (GPs) in handling the correlated noise caused by the variable source. We find that the parameters from the GP model are generally more loosely constrained than those from the traditional χ(exp 2) minimization method. We note that this event is the first microlensing system for which asteroseismology and GPs have been used to account for the variable source. With both finite-source effect and microlens parallax measured, we find that the lens is likely a ∼0.045 Mʘ brown dwarf at distance ∼9.0 kpc, or a ∼0.073 Mʘ ultracool dwarf at distance ∼9.8 kpc. Combining the estimated lens properties with a Bayesian analysis using a Galactic model, we find a ∼ 35 per cent probability for the lens to be a bulge object and ∼ 65 per cent to be a background disc object.

S.-S. Li↗
NASA NTRSDate not supplied

A Gaussian Process Enhancement to Linear Parameter Varying Models

Simulation and analysis for modern engineering systems now routinely requires the merging of multiple disciplines, physical-domains, time-scales, and data sets — all at ever increasing levels. These capabilities are especially needed in the domain of Advanced Air Mobility, where rapidly emerging vehicle designs are significantly more complex, while having to be both cost-effective and safe. To meet these engineering challenges, machine learning methods are an attractive option for merging models and data across multiple areas while providing uncertainty quantification and maintaining computational efficiency. This paper examines the use of Gaussian process machine learning to generalize and enhance the commonly used class of quasi-Linear Parameter Varying models for fast full-envelope simulation while also supporting control system design and analysis with model uncertainty. Gaussian process machine learning is selected because it: can fuse multiple data sets, enables an easy trade-off between data fitting and smoothing, provides model uncertainty quantification, scales well with increasing complexity, and does not generally require starting from a large training data set. To demonstrate the benefits of the approach, a robust stability analysis with Gaussian process uncertainty is shown for a NASA reference design of an electric quad-rotor air-taxi concept vehicle with motor parameter uncertainty.

Gaussian Process↗
NASA NTRS2013

Simulation of Inflated Pahoehoe Lava Flows

A new stochastic model simulates late-stage pahoehoe lobes where random processes dominate emplacement. The model prescribes probabilistic rules for determining where and when parcels of lava move within the lobe. Unlike a classical Brownian motion random walk, the model allows individual parcels to remain dormant, but fluid, for multiple time steps. The randomness of parcel volume transfers within the lobe interior as well as at the margins qualitatively reflects inflation processes observed in the field. The fraction of inflated volume to total volume increases with the total volume, with greater than 75% of the lobe volume contributed through inflation for typical lobes. The influence on planform shape and topographic cross-sectional profiles of total volume, source area and shape, topographic confinement, and sequential breakouts at the lobe margins, are all explored with the stochastic model. Each of these factors influences the overall lobe thickness and width. The model provides a means for assessing the relative importance of these processes through comparisons with field data. For the first time, Gaussian and parabolic functions are quantitatively fit to field measurements of pahoehoe lobes. Both functional forms provide adequate description of the cross-sectional flow shapes. When comparing simulated lobes to field data, sequential breakouts at the lobe margins are found to be an important process controlling the final topographic distribution of observed pahoehoe lobes.

modeling↗
NASA NTRS1999

In Pursuit of the FIP Effect in Late-Type Stellar Coronae

Spectral line data for several coronally active stars, in addition to EUVE Deep Survey light curves, have been analysed under this program. Much difficulty has been encountered in the study that has resulted in fewer stars being analysed than had been hoped. The difficulties stemmed from the analysis of low X-ray spectra taken with the ASCA satellite that produced results that are strongly discrepant with respect to the EUVE results. There is no obvious explanation for this, though it appears that analysis of ASCA data systematically underestimate metal abundance in hot plasmas. Consequently, the final emphasis in our analyses has been on EUVE data. Observed line profiles have being fitted in order to measure their fluxes using IDL software specially developed under this and parallel efforts.The observed line profiles deviate from pure gaussian forms, but we have found the benefits of using additional functional forms in the fitting process to be of only very small value for the lines with highest S/N. The resulting line fluxes have being processed in terms of the coronal EM using new techniques. Resulting EM distribution models are being used to finalize metallicity and abundance estimates for the stars in the program. Special account of the influence of missing lines in the spectral models has been taken.

Drake, Jeremy↗
NASA NTRS2018

Realistic Covariance Generation for the GPM Spacecraft

A covariance realism process for NASA's Global Precipitation Measurement (GPM) spacecraft is detailed. The GPM spacecraft is in a low earth orbit, and performs collision avoidance maneuvers few times a year. Currently GPM is below the International Space Station (ISS). So, in addition to cataloged debris objects, GPM must contend with smallsat/cubesat objects that are deployed from the ISS. Both operational scenarios require complete knowledge of the expected GPM prediction errors as a function of time. In this study, we present a method for generating realistic predicted covariance that uses linear propagation of the covariance with the addition of process noise. Further analyses are presented for the process noise ''tuning'' that generates an inflation factor based on the observed error statistics of the predictive satellite trajectories when compared to the definitive ones. Different tuning strategies are considered and compared via a Goodness-of-Fit testing for the Gaussian properties of the scaled covariance. SpaceNav's realistic covariance generation approach takes into account the contribution of predicted maneuver errors in the increased propagation uncertainty. Corresponding maneuver uncertainty is injected into the state uncertainty, and is used within the collision avoidance process to determine the collision risk for close approach events that follow a maneuver. This is a critical step in the maneuver planning process that provides the satellite operator with an accurate quantification of the collision probability for planned maneuvers. Using this information, an informed decision can be made to proceed with a maneuver if the collision risk is acceptable. This approach is validated by Monte-Carlo simulations and results are presented.

spacecraft uncertainty propagation↗
NASA NTRS2013

Incorporating Skew into RMS Surface Roughness Probability Distribution

The standard treatment of RMS surface roughness data is the application of a Gaussian probability distribution. This handling of surface roughness ignores the skew present in the surface and overestimates the most probable RMS of the surface, the mode. Using experimental data we confirm the Gaussian distribution overestimates the mode and application of an asymmetric distribution provides a better fit. Implementing the proposed asymmetric distribution into the optical manufacturing process would reduce the polishing time required to meet surface roughness specifications.

Stahl, Mark T.↗
NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗
NASA NTRSDate not supplied

Influence of Thermophysical Property Variability on Thermal Predictions of Ti-6Al-4V

The additive manufacturing (AM) industry has experienced rapid growth in recent decades as industrial interest in the process has grown. Because of this, there are many groups interested in simulations of the AM process. However, the influence of thermophysical property variability on the melt pool geometry during processing of various AM alloys is unclear. The goal of my work at NASA Langley Research Center (LaRC) was to characterize this influence on Ti-6Al-4V during AM processing alongside developing a tool to enable equivalent studies on other AM materials. To facilitate this process, a database tool was developed to contain and organize thermophysical data previously reported by primary sources. The specific thermophysical properties of interest for this study were density, specific heat capacity, and conductivity. Thermal diffusivity was calculated using the other three properties. This data was then fit with a Gaussian distribution and then randomly sampled from using Monte Carlo random value sampling. Using the Rosenthal equation, this sample data was used to simulate the temperature field during additive manufacturing and extract the melt pool geometry. Specifically, the melt pool’s length, width, and depth. This process was repeated an arbitrary number of times, with the default being 1000. Once this information was obtained, histograms were made showing the distributions of the sizes of the melt pool’s length, width, and depth. Representative statistical metrics of the distributions were calculated (mean, standard deviation, and coefficient of variance). This work found that at the simulated processing values, the melt pool’s width and depth had a 5.7% variation while the length had a 3.2% variation. Additionally, a tool was constructed to graph a thermal color map representation of the melt pool for specific, arbitrary values of density, specific heat, and conductivity. This tool allowed for more efficient plotting of individual queries of the thermophysical properties. The results that were observed in the research were that values reported in literature vary and this variance can have a significant impact on the simulation. Predictions of the melt pool’s dimensions show that length has a standard deviation of approximately 6 μm, width has a standard deviation of approximately 5 μm, and depth can vary by approximately 3 μm. Considering the mean sizes of length, width, and depth are 185 μm, 88 μm, and 44 μm respectively, such a deviation is significant. This shows that depth, for example, could be more than 10% larger or smaller than expected. This demonstrates that variability in the reported thermophysical properties are not negligible and should be expected to have an influence on the results of the laser powder bed fusion additive manufacturing process. When simulating this process in the future, measures should be taken to account for this discrepancy and the uncertainty involved

Justin Martin↗
NASA NTRS2022

Gaussian Process Regression Method for Costing SmallSat Bus Capabilities

NASA is responding to the growing interest in, andcapabilities of, small satellites for science applications with an increasingnumber and frequency of Announcements of Opportunityfor small satellite space missions. Estimating the probabilitythat these mission concepts will fit within the small cost capsof these opportunities is largely driven by the probability thatone of the burgeoning number of small satellite providers will beable to meet the payload’s accommodation requirements withinthe budget for the spacecraft. JPL has collected a databasecontaining technical specifications and cost of commerciallyavailable Smallsat buses across various vendors. The primarypurpose of the database is for use in JPL’s Team X architecturestudies to inform cost estimates of a spacecraft bus which fitsthe customer’s technical requirements for their payload andmission. Customer needs are often unique and don’t alignperfectly with an off-the-shelf commercial spacecraft bus, whichmotivates the need to develop a cost model across the continuoustechnical parameter space.Al’s Bus Cost Distribution Estimator (ABCDE) uses Gaussianprocess regression (GPR) to predict commercial Smallsat spacecraftbus cost based on a subset of a customer’s technicalrequirements (payload mass, payload power, delta V, pointingcontrol, and downlink rate). GPR is implemented in ABCDE asa Bayesian method which fits an implied multivariate regressionon the technical parameters and uses kriging to intentionally“overfit” the residuals. Overfitting the residuals allows costestimates to collapse in uncertainty closer to the data pointswhile maintaining larger uncertainty intervals in regions of parameterspace with fewer data records. The data used to fit thismodel is sensitive and represents cost estimates for off-the-shelfcommercial buses. GPR simultaneously protects the sensitivityof the database and uses the sparse nature of the database toaccount for uncertainty in cost in a useful way. For a givenset of customer technical requirements, the tool provides a costestimate distribution, the percentiles of which can be interpretedas a confidence level of finding a commercial bus under a specifiedcost cap. ABCDE dramatically pushes the boundaries ofspacecraft cost estimation models due to its Bayesian methodology(accounting for the maximum uncertainty in the underlyingregression), the mathematically advanced kriging methodology,and the novelty of its application in Team X architecture tradestudies.

Austin, Alex↗
NASA NTRS2010

Automatic Identification of Solar X-Ray Bright Points in Hinode X-Ray Data

We have automated a method that is used to find point sources in Chandra X-ray telescope data, to identify solar bright points in Hinode X-ray data. This tool, called lextrct, first identifies candidate sources that are brighter than the surrounding background. The algorithm also allows selected pixels to be excluded from the source-finding, thus allowing saturated pixels (from flares and/or active regions) to be ignored. We then use lextrct to fit the sources to two-dimensional, elliptical Gaussians. The size and orientation give an approximation of the shape of the bright points. We are in the process of analyzing observations through the Al_poly filter with a four-second exposure time, to obtain a catalogue of bright points, which will include their sizes, lifetimes, intensities, and position on the solar disk

Adams, M. L.↗
NASA NTRS2018

Realistic Covariance Generation for the GPM Spacecraft

We present several different methods for generating realistic predictive covariance, including Monte-Carlo simulations and more direct linear methods which require the addition of process noise. The Monte-Carlo simulation starts with an epoch uncertainty sample basis and propagates each trial to a time of interest in the future. The variance-covariance of the state elements as well as other higher order sample statistics can be readily computed from the propagated sample. While this method preserves the nonlinear effects on the propagated uncertainty, it is computationally intensive as a statistically significant sample size must be considered in the propagation process. Moreover, if the epoch covariance is optimistic, this can result in an underestimation of the prediction error. Another method is to propagate a state sensitivity matrix simultaneously with the satellite state, which allows the epoch covariances to be propagated forward in a linear fashion. This method does not preserve the non-linearity of the satellite state uncertainty but is much less computationally intensive. The propagated state covariance is the scaled to represent the realistic level of GPM state uncertainty via a "e-tuning process." The tuning process generates an inflation factor based on the observed error statistics of the predictive satellite trajectories when compared to the definitive ones. Difference tuning strategies are considered and compared via Goodness-of-Fit method testing for the Gaussian properties of the scaled covariance.

prediction error↗
NASA NTRS1992

The cool-star spectral catalog: A uniform collection of IUE SWP-LOs

Over the past decade and a half of its operations, the International Ultraviolet Explorer has recorded low-dispersion spectrograms in the 1150-2000 A interval of more than 800 stars of late spectral type (F-M). The sub-2000 A region contains a number of emission lines that are key diagnostics of physical conditions in the high-excitation chromospheres and subcoronal 'transition zones' of such stars. Many of the sources have been observed a number of times, and the available collection of SWP-LO exposures in the IUE Archives exceeds 4,000. With support from the Astrophysics Data Program, we have assembled the archival material into a catalog of IUE far-UV fluxes of late-type stars. In order to ensure uniform processing of the spectra, we: (1) photometrically corrected the raw vidicon images with a custom version of the 1985 SWP ITF; (2) identified and eliminated, sharp cosmic-ray 'hits' by means of a spatial filter; (3) extracted the spectral traces with the 'optimal' (weighted-slit) strategy; and (4) calibrated them against a well-characterized reference source, the DA white dwarf G191-B2B. Our approach is similar to that adopted by the IUE Project for its 'Final Archive', but our implementation is specialized to the case of chromospheric emission-line sources. We measured the resulting SWP-LO spectra using a semi-autonomous algorithm that establishes a smooth continuum by numerical filtering, and then fits the significant emissions (or absorptions) by means of a constrained Bevington-type multiple-Gaussian procedure. The algorithm assigns errors to the fitted fluxes - or upper limits in the absence of a significant detection - according to a model based on careful measurements of the noise properties of the IUE's intensified SEC cameras. Here, we describe the 'visualization' strategies we adopted to ensure human-review of the semi-autonomous processing and measuring algorithms; the derivation of the noise model and the assignment of errors; and the structure of the final catalog as delivered to the Astrophysics Data System.

Ayres, T.↗
NASA NTRS1976

Some new results on the statistics of radio wave scintillation. I - Empirical evidence for Gaussian statistics

This paper presents an analysis of ionospheric scintillation data which shows that the underlying statistical structure of the signal can be accurately modeled by the additive complex Gaussian perturbation predicted by the Born approximation in conjunction with an application of the central limit theorem. By making use of this fact, it is possible to estimate the in-phase, phase quadrature, and cophased scattered power by curve fitting to measured intensity histograms. By using this procedure, it is found that typically more than 80% of the scattered power is in phase quadrature with the undeviated signal component. Thus, the signal is modeled by a Gaussian, but highly non-Rician process. From simultaneous UHF and VHF data, only a weak dependence of this statistical structure on changes in the Fresnel radius is deduced. The signal variance is found to have a nonquadratic wavelength dependence. It is hypothesized that this latter effect is a subtle manifestation of locally homogeneous irregularity structures, a mathematical model proposed by Kolmogorov (1941) in his early studies of incompressible fluid turbulence.

Rino, C. L.↗
NASA NTRS2010

Energy Measurement Studies for CO2 Measurement with a Coherent Doppler Lidar System

The accurate measurement of energy in the application of lidar system for CO2 measurement is critical. Different techniques of energy estimation in the online and offline pulses are investigated for post processing of lidar returns. The cornerstone of the techniques is the accurate estimation of the spectrum of lidar signal and background noise. Since the background noise is not the ideal white Gaussian noise, simple average level estimation of noise level is not well fit in the energy estimation of lidar signal and noise. A brief review of the methods is presented in this paper.

Beyon, Jeffrey Y.↗
NASA NTRS1994

An extended source for CN jets in Comet P/Halley

We examined radial intensity profiles of CN jets in comparison with the diffuse, isotropic component of the CN coma of Comet P/Halley. All images were bias-subtracted, flat-fielded, and continuum-subtracted. We calculated the diffuse profiles by finding the azimuthal mean of the coma least contaminated by jets yielding profiles similar to those of vectorial and Haser models of simple photodissociation. We found the jet profiles by calculating a mean around a Gaussian-fitted center in r-theta space. There is an unmistakable difference between the profiles of the CN jets and the profiles of the diffuse CN. Spatial derivatives of these profiles, corrected for geometrical expansion, show that the diffuse component is consistent with a simple photodissociation process, but the jet component is not. The peak production of the jet profile occurs 6000 km from the nucleus at a heliocentric distance of 1.4 AU. Modeling of both components of the coma indicate results that are consistent with the diffuse CN photochemically produced, but the CN jets need an additional extended source. We found that about one-half of the CN in the coma of Comet P/Halley originated from the jets, the rest from the diffuse component. These features, along with the width of the jet being approximately constant, are consistent with a CHON grain origin for the jets.

Klavetter, James Jay↗